@article{Elahi2022,

title = {Developing and Evaluating a University Recommender System},

author = {Mehdi Elahi and Alain D. Starke and Nabil El Ioini and Anna Alexander Lambrix and Christoph Trattner},

url = {https://www.frontiersin.org/articles/10.3389/frai.2021.796268/full},

doi = {https://doi.org/10.3389/frai.2021.796268},

year  = {2022},

date = {2022-02-02},

journal = {Frontiers in Artificial Intelligence },

abstract = {A challenge for many young adults is to find the right institution to follow higher education. Global university rankings are a commonly used, but inefficient tool, for they do not consider a person's preferences and needs. For example, some persons pursue prestige in their higher education, while others prefer proximity. This paper develops and evaluates a university recommender system, eliciting user preferences as ratings to build predictive models and to generate personalized university ranking lists. In Study 1, we performed offline evaluation on a rating dataset to determine which recommender approaches had the highest predictive value. In Study 2, we selected three algorithms to produce different university recommendation lists in our online tool, asking our users to compare and evaluate them in terms of different metrics (Accuracy, Diversity, Perceived Personalization, Satisfaction, and Novelty). We show that a SVD algorithm scores high on accuracy and perceived personalization, while a KNN algorithm scores better on novelty. We also report findings on preferred university features.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Starke2021,

title = {Nudging Healthy Choices in Food Search Through Visual Attractiveness},

author = {Alain D. Starke and Martijn C. Willemsen and Christoph Trattner},

url = {https://www.frontiersin.org/articles/10.3389/frai.2021.621743/full},

doi = {10.3389/frai.2021.621743},

year  = {2021},

date = {2021-04-22},

number = {April 2021},

pages = {1-18},

abstract = {Recipe websites are becoming increasingly popular to support people in their home cooking. However, most of these websites prioritize popular recipes, which tend to be unhealthy. Drawing upon research on visual biases and nudges, this paper investigates whether healthy food choices can be supported in food search by depicting attractive images alongside recipes, as well as by re-ranking search results on health. After modelling the visual attractiveness of recipe images, we asked 239 users to search for specific online recipes and to select those they liked the most. Our analyses revealed that users tended to choose a healthier recipe if a visually attractive image was depicted alongside it, as well as if it was listed at the top of a list of search results. Even though less popular recipes were promoted this way, it did not come at the cost of a user’s level of satisfaction},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Endriss2020,

title = {The complexity landscape of outcome determination in judgment aggregation},

author = {Ulle Endriss and Ronald de Haan and Jerôme Lang and Marija Slavkovik },

url = {https://www.jair.org/index.php/jair/article/view/11970/26619},

doi = {10.1613/jair.1.11970},

year  = {2020},

date = {2020-11-04},

journal = {Journal of Artificial Intelligence Research},

volume = {69},

pages = {687–731},

abstract = {We provide a comprehensive analysis of the computational complexity of the outcome determinationproblem for the most important aggregation rules proposed in the literature on logic-based judgmentaggregation. Judgment aggregation is a powerful and flexible framework for studying problems ofcollective decision making that has attracted interest in a range of disciplines, including Legal Theory,Philosophy, Economics, Political Science, and Artificial Intelligence. The problem of computing theoutcome for a given list of individual judgments to be aggregated into a single collective judgment isthe most fundamental algorithmic challenge arising in this context. Our analysis applies to severaldifferent variants of the basic framework of judgment aggregation that have been discussed in theliterature, as well as to a new framework that encompasses all existing such frameworks in terms ofexpressive power and representational succinctness.},

note = {Pre SFI},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Hazrati2020,

title = { Addressing the New Item problem in video recommender systems by incorporation of visual features with restricted Boltzmann machines.},

author = {Naieme Hazrati and Mehdi Elahi},

url = {https://onlinelibrary.wiley.com/doi/epdf/10.1111/exsy.12645},

doi = {https://doi.org/10.1111/exsy.12645},

year  = {2020},

date = {2020-10-19},

journal = {Expert Systems},

volume = {e12645},

pages = {1-20},

abstract = {Over the past years, the research of video recommender systems (RSs) has been mainly focussed on the development of novel algorithms. Although beneficial, still any algorithm may fail to recommend video items that the system has no form of data associated to them (New Item Cold Start). This problem occurs when a new item is added to the catalogue of the system and no data are available for that item. In content‐based RSs, the video items are typically represented by semantic attributes, when generating recommendations. These attributes require a group of experts or users for annotation, and still, the generated recommendations might not capture a complete picture of the users' preferences, for example, the visual tastes of users on video style. This article addresses this problem by proposing recommendation based on novel visual features that do not require human annotation and can represent visual aspects of video items. We have designed a novel evaluation methodology considering three realistic scenarios, that is, (a) extreme cold start, (b) moderate cold start and (c) warm‐start scenario. We have conducted a set of comprehensive experiments, and our results have shown the superior performance of recommendations based on visual features, in all of the evaluation scenarios.},

note = {Pre SFI},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Nunes2020,

title = {A Systematic Review and Taxonomy of Explanations in Decision Support and Recommender Systems},

author = {Ingrid Nunes and Dietmar Jannach},

url = {https://arxiv.org/pdf/2006.08672.pdf},

doi = {10.1007/s11257-017-9195-0},

year  = {2020},

date = {2020-06-15},

journal = {User-Modeling and User-Adapted Interaction},

volume = {27},

number = {3-5},

pages = {393-444},

abstract = {With the recent advances in the field of artificial intelligence, an increasing number of decision-making tasks are delegated to software systems. A key requirement for the success and adoption of such systems is that users must trust system choices or even fully automated decisions. To achieve this, explanation facilities have been widely investigated as a means of establishing trust in these systems since the early years of expert systems. With today's increasingly sophisticated machine learning algorithms, new challenges in the context of explanations, accountability, and trust towards such systems constantly arise. In this work, we systematically review the literature on explanations in advice-giving systems. This is a family of systems that includes recommender systems, which is one of the most successful classes of advice-giving software in practice. We investigate the purposes of explanations as well as how they are generated, presented to users, and evaluated. As a result, we derive a novel comprehensive taxonomy of aspects to be considered when designing explanation facilities for current and future decision support systems. The taxonomy includes a variety of different facets, such as explanation objective, responsiveness, content and presentation. Moreover, we identified several challenges that remain unaddressed so far, for example related to fine-grained issues associated with the presentation of explanations and how explanation facilities are evaluated.},

note = {Pre SFI},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Sjøvaag2020,

title = {Strategic and Organisational fit in Corporate News Markets: A Principal-agent Approach to Studying Newspaper Mergers},

author = {Helle Sjøvaag and Thomas Owren and Turid Borgen},

url = {https://www.tandfonline.com/doi/epub/10.1080/17512786.2020.1772097?needAccess=true},

doi = {https://doi.org/10.1080/17512786.2020.1772097},

year  = {2020},

date = {2020-05-20},

journal = {Journalism Practice},

pages = {1-18},

abstract = {This article analyses strategic and organisational fit in corporate newspaper mergers in the context of the digitalisation of local newspaper markets. Using the 2019 acquisition of Nordsjø Media by Amedia in Norway as case, we analyse how eight editors-in-chief perceive the process of incorporating small, low-frequency, print-oriented monopolistic newspapers into one of Scandinavia’s largest newspaper chains. The semi-structured interviews were analysed in light of perceived strategic and organisational fit in a principal-agent theoretical framework, the aim of which is to shed light on corporate ownership effects in consolidated newspaper markets. The analysis reveals the precarity of independent ownership in digitising news markets, to which corporatisation emerges as a necessary and welcomed solution. We find the strategic fit as perceived by editors to be tied to technological resources and scale economics, while organisational fit is hampered by the speed and pressure of corporatisation processes. While these results largely support findings from previous acquisition studies in the news industries, the contribution of this analysis lies primarily with the necessity of scale required by the technological transformation that forces independent newspapers to submit to larger chain operations and how it influences considerations of fit in disruptive digital news markets.},

note = {Pre SFI},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Zhang2020,

title = {Web Table Extraction, Retrieval, and Augmentation: A Survey},

author = {Shuo Zhang and Krisztian Balog},

url = {https://arxiv.org/pdf/2002.00207.pdf},

year  = {2020},

date = {2020-02-01},

journal = {ACM Transactions on Intelligent Systems and Technology (TIST)},

volume = {11},

number = {2},

pages = {1-35},

abstract = {Tables are a powerful and popular tool for organizing and manipulating data. A vast number of tables can be found on the Web, which represents a valuable knowledge resource. The objective of this survey is to synthesize and present two decades of research on web tables. In particular, we organize existing literature into six main categories of information access tasks: table extraction, table interpretation, table search, question answering, knowledge base augmentation, and table augmentation. For each of these tasks, we identify and describe seminal approaches, present relevant resources, and point out interdependencies among the different tasks.},

note = {Pre SFI},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Sjøvaag2018c,

title = {Is public service broadcasting a threat to commercial media?},

author = {Helle Sjøvaag and Truls André Pedersen and Thomas Owren},

url = {https://journals.sagepub.com/doi/pdf/10.1177/0163443718818354},

doi = {https://doi.org/10.1177/0163443718818354},

year  = {2018},

date = {2018-12-20},

journal = {Media, Culture & Society},

volume = {46},

number = {1},

pages = {808-827},

abstract = {This article asks to what extent public service broadcasting’s online news service resembles that of commercial media. The context of this inquiry is claims of ‘out-crowding’ facing public service broadcasters across Europe. In Norway, commercial players in this debate accuse the public service broadcaster, NRK, of being too similar to competitors in the private sector for commercial operators to attain sustainable revenues in the online realm. To ascertain the extent to what these claims are warranted, this article compares NRK’s online content with that of nine commercial competitors in national and local markets, using a hybrid methodological approach combining quantitative content analysis with Latent Dirichlet allocation, analysing in excess of 115,000 documents. Findings show that commercial operators resemble each other more than they do NRK, indicating closer competition in the commercial segment than between the public service broadcaster and market players.},

note = {Pre SFI},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Sjøvaag2018b,

title = {Female voices in the news: Structural conditions of gender representations in Norwegian newspapers},

author = {Helle Sjøvaag and Truls André Pedersen},

url = {https://journals.sagepub.com/doi/pdf/10.1177/1077699018789885},

doi = {https://doi.org/10.1177/1077699018789885},

year  = {2018},

date = {2018-08-21},

journal = {Journalism & Mass Communication Quarterly},

volume = {96},

number = {1},

pages = {215-238},

abstract = {The article presents a hybrid analysis combining manual content analysis of 9,131 sources in 5,544 news stories across 75 publications, with computational gender recognition producing 551,102 names from 320,228 articles across 125 newspapers. The article investigates the significance of structural features for the presence of women in the news. Results show female sources are only equal to men as ordinary citizens and children, and only in lifestyle content. Among the structural features examined, only local distribution and a circulation less than 5,000 exhibit improvements in female representation. Ownership, distribution frequency, market position, and direct press support had little or no effect.},

note = {Pre SFI},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Sjøvaag2018,

title = {The hyperlinked Scandinavian news ecology: The unequal terms forged by the structural properties of digitalisation},

author = {Helle Sjøvaag and Eirik Stavelin and Michael Karlsson and Aske Kammer},

url = {https://www.tandfonline.com/doi/epub/10.1080/21670811.2018.1454335?needAccess=true},

doi = {https://doi.org/10.1080/21670811.2018.1454335},

year  = {2018},

date = {2018-04-06},

journal = {Digital Journalism},

volume = {7},

number = {4},

pages = {507-531},

abstract = {The article presents a network analysis of 22,861,013 geocoded external hyperlinks, collected from 230 Danish, 220 Norwegian and 208 Swedish news websites in 2016. The analysis asks what the structural properties of the Scandinavian media systems—including its geography and ownership structures—mean for news outlets’ centrality within the hyperlinked news ecology. The analysis finds that whereas incumbent legacy media occupy central positions, about one third of the network is absent from the hyperlinked interaction, primarily local, independently owned newspapers. A multiple linear regression analysis shows that national distribution and corporate ownership correlates to network centrality more than other predictors. As brokers in the network consist of the large, legacy, capital-based news organisations, hyperlink connectivity is primarily characterised by proximity to the centres of power, corporate ownership, agenda setting incumbency and national distribution.},

note = {Pre SFI},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Ludewig2018,

title = {Evaluation of Session-based Recommendation Algorithms},

author = {Malte Ludewig and Dietmar Jannach},

url = {https://arxiv.org/pdf/1803.09587.pdf},

doi = {10.1007/s11257-018-9209-6},

year  = {2018},

date = {2018-03-26},

journal = {User-Modeling and User-Adapted Interaction},

volume = {28},

number = {4-5},

pages = {331-390},

abstract = {Recommender systems help users find relevant items of interest, for example on e-commerce or media streaming sites. Most academic research is concerned with approaches that personalize the recommendations according to long-term user profiles. In many real-world applications, however, such long-term profiles often do not exist and recommendations therefore have to be made solely based on the observed behavior of a user during an ongoing session. Given the high practical relevance of the problem, an increased interest in this problem can be observed in recent years, leading to a number of proposals for session-based recommendation algorithms that typically aim to predict the user's immediate next actions. In this work, we present the results of an in-depth performance comparison of a number of such algorithms, using a variety of datasets and evaluation measures. Our comparison includes the most recent approaches based on recurrent neural networks like GRU4REC, factorized Markov model approaches such as FISM or FOSSIL, as well as simpler methods based, e.g., on nearest neighbor schemes. Our experiments reveal that algorithms of this latter class, despite their sometimes almost trivial nature, often perform equally well or significantly better than today's more complex approaches based on deep neural networks. Our results therefore suggest that there is substantial room for improvement regarding the development of more sophisticated session-based recommendation algorithms.},

note = {Pre SFI},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Quadrana2018,

title = {Sequence-Aware Recommender Systems},

author = {Massimo Quadrana and Paolo Cremonesi and Dietmar Jannach},

url = {https://arxiv.org/pdf/1802.08452.pdf},

year  = {2018},

date = {2018-02-23},

journal = {ACM Computing Surveys},

volume = {51},

number = {4},

pages = {1-35},

abstract = {Recommender systems are one of the most successful applications of data mining and machine learning technology in practice. Academic research in the field is historically often based on the matrix completion problem formulation, where for each user-item-pair only one interaction (e.g., a rating) is considered. In many application domains, however, multiple user-item interactions of different types can be recorded over time. And, a number of recent works have shown that this information can be used to build richer individual user models and to discover additional behavioral patterns that can be leveraged in the recommendation process. In this work we review existing works that consider information from such sequentially-ordered user- item interaction logs in the recommendation process. Based on this review, we propose a categorization of the corresponding recommendation tasks and goals, summarize existing algorithmic solutions, discuss methodological approaches when benchmarking what we call sequence-aware recommender systems, and outline open challenges in the area.},

note = {Pre SFI},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Jannach2016,

title = {Recommender Systems - Beyond Matrix Completion},

author = {Dietmar Jannach and Alexander Tuzhilin and Markus Zanker },

url = {https://www.researchgate.net/publication/309600906_Recommender_systems---_beyond_matrix_completion},

doi = {10.1145/2891406},

year  = {2016},

date = {2016-10-01},

journal = {Communications of the ACM},

volume = {59},

number = {11},

pages = {94-102},

abstract = {Recommender systems have become a natural part of the user experience in today's online world. These systems are able to deliver value both for users and providers and are one prominent example where the output of academic research has a direct impact on the advancements in industry. In this article, we have briefy reviewed the history of this multidis-ciplinary field and looked at recent efforts in the research community to consider the variety of factors that may influence the long-term success of a recommender system. The list of open issues and success factors is still far from complete and new challenges arise constantly that require further research. For example, the huge amounts of user data and preference signals that become available through the Social Web and the Internet of Things not only leads to technical challenges such as scalability, but also to societal questions concerning user privacy. Based on our reflections on the developments in the field, we finally emphasize the need for a more holistic research approach that combines the insights of different disciplines. We urge that research focuses even more on practical problems that matter and are truly suited to increase the utility of recommendations from the viewpoint of the users.

},

note = {Pre SFI},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Elahi2016,

title = {A survey of active learning in collaborative filtering recommender systems},

author = {Mehdi Elahi and Francesco Ricci and Neil Rubens},

url = {https://reader.elsevier.com/reader/sd/pii/S1574013715300150?token=EA12A462FC07F42733F4F13375217A57D3FDC7F6047C133156CB1F4E4487DF24C5366547DF4530A25942F690233F2E30},

doi = {10.1016/j.cosrev.2016.05.002},

year  = {2016},

date = {2016-06-02},

journal = {Computer Science Review},

volume = {20},

pages = {29-50},

abstract = {In collaborative filtering recommender systems user’s preferences are expressed as ratings for items, and each additional rating extends the knowledge of the system and affects the system’s recommendation accuracy. In general, the more ratings are elicited from the users, the more effective the recommendations are. However, the usefulness of each rating may vary significantly, i.e., different ratings may bring a different amount and type of information about the user’s tastes. Hence, specific techniques, which are defined as “active learning strategies”, can be used to selectively choose the items to be presented to the user for rating. In fact, an active learning strategy identifies and adopts criteria for obtaining data that better reflects users’ preferences and enables to generate better recommendations.



So far, a variety of active learning strategies have been proposed in the literature. In this article, we survey recent strategies by grouping them with respect to two distinct dimensions: personalization, i.e., whether the system selected items are different for different users or not, and, hybridization, i.e., whether active learning is guided by a single criterion (heuristic) or by multiple criteria. In addition, we present a comprehensive overview of the evaluation methods and metrics that have been employed by the research community in order to test active learning strategies for collaborative filtering. Finally, we compare the surveyed strategies and provide guidelines for their usage in recommender systems.},

note = {Pre SFI},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Tobias2016,

title = {Alleviating the new user problem in collaborative filtering by exploiting personality information},

author = {Ignacio Fernandez Tobias and Matthias Braunhofer and Mehdi Elahi and Francesco Ricci and Ivan Cantador},

url = {https://www.researchgate.net/publication/285574429_Alleviating_the_New_User_Problem_in_Collaborative_Filtering_by_Exploiting_Personality_Information},

doi = {10.1007/s11257-016-9172-z},

year  = {2016},

date = {2016-06-01},

journal = {User Modeling and User-Adapted Interaction},

volume = {26},

number = {2-3},

pages = {221-255},

abstract = {The new user problem in recommender systems is still challenging, and there is not yet a unique solution that can be applied in any domain or situation. In this paper we analyze viable solutions to the new user problem in collaborative filtering (CF) that are based on the exploitation of user personality information: (a) personality-based CF, which directly improves the recommendation prediction model by incorporating user personality information, (b) personality-based active learning, which utilizes personality information for identifying additional useful preference data in the target recommendation domain to be elicited from the user, and (c) personality-based cross-domain recommendation, which exploits personality information to better use user preference data from auxiliary domains which can be used to compensate the lack of user preference data in the target domain. We benchmark the effectiveness of these methods on large datasets that span several domains, namely movies, music and books. Our results show that personality-aware methods achieve performance improvements that range from 6 to 94 % for users completely new to the system, while increasing the novelty of the recommended items by 3–40 % with respect to the non-personalized popularity baseline. We also discuss the limitations of our approach and the situations in which the proposed methods can be better applied, hence providing guidelines for researchers and practitioners in the field.},

note = {Pre SFI},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Elahi2022,

title = {Developing and Evaluating a University Recommender System},

author = {Mehdi Elahi and Alain D. Starke and Nabil El Ioini and Anna Alexander Lambrix and Christoph Trattner},

url = {https://www.frontiersin.org/articles/10.3389/frai.2021.796268/full},

doi = {https://doi.org/10.3389/frai.2021.796268},

year  = {2022},

date = {2022-02-02},

journal = {Frontiers in Artificial Intelligence },

abstract = {A challenge for many young adults is to find the right institution to follow higher education. Global university rankings are a commonly used, but inefficient tool, for they do not consider a person's preferences and needs. For example, some persons pursue prestige in their higher education, while others prefer proximity. This paper develops and evaluates a university recommender system, eliciting user preferences as ratings to build predictive models and to generate personalized university ranking lists. In Study 1, we performed offline evaluation on a rating dataset to determine which recommender approaches had the highest predictive value. In Study 2, we selected three algorithms to produce different university recommendation lists in our online tool, asking our users to compare and evaluate them in terms of different metrics (Accuracy, Diversity, Perceived Personalization, Satisfaction, and Novelty). We show that a SVD algorithm scores high on accuracy and perceived personalization, while a KNN algorithm scores better on novelty. We also report findings on preferred university features.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Starke2021,

title = {Nudging Healthy Choices in Food Search Through Visual Attractiveness},

author = {Alain D. Starke and Martijn C. Willemsen and Christoph Trattner},

url = {https://www.frontiersin.org/articles/10.3389/frai.2021.621743/full},

doi = {10.3389/frai.2021.621743},

year  = {2021},

date = {2021-04-22},

number = {April 2021},

pages = {1-18},

abstract = {Recipe websites are becoming increasingly popular to support people in their home cooking. However, most of these websites prioritize popular recipes, which tend to be unhealthy. Drawing upon research on visual biases and nudges, this paper investigates whether healthy food choices can be supported in food search by depicting attractive images alongside recipes, as well as by re-ranking search results on health. After modelling the visual attractiveness of recipe images, we asked 239 users to search for specific online recipes and to select those they liked the most. Our analyses revealed that users tended to choose a healthier recipe if a visually attractive image was depicted alongside it, as well as if it was listed at the top of a list of search results. Even though less popular recipes were promoted this way, it did not come at the cost of a user’s level of satisfaction},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Endriss2020,

title = {The complexity landscape of outcome determination in judgment aggregation},

author = {Ulle Endriss and Ronald de Haan and Jerôme Lang and Marija Slavkovik },

url = {https://www.jair.org/index.php/jair/article/view/11970/26619},

doi = {10.1613/jair.1.11970},

year  = {2020},

date = {2020-11-04},

journal = {Journal of Artificial Intelligence Research},

volume = {69},

pages = {687–731},

abstract = {We provide a comprehensive analysis of the computational complexity of the outcome determinationproblem for the most important aggregation rules proposed in the literature on logic-based judgmentaggregation. Judgment aggregation is a powerful and flexible framework for studying problems ofcollective decision making that has attracted interest in a range of disciplines, including Legal Theory,Philosophy, Economics, Political Science, and Artificial Intelligence. The problem of computing theoutcome for a given list of individual judgments to be aggregated into a single collective judgment isthe most fundamental algorithmic challenge arising in this context. Our analysis applies to severaldifferent variants of the basic framework of judgment aggregation that have been discussed in theliterature, as well as to a new framework that encompasses all existing such frameworks in terms ofexpressive power and representational succinctness.},

note = {Pre SFI},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Hazrati2020,

title = { Addressing the New Item problem in video recommender systems by incorporation of visual features with restricted Boltzmann machines.},

author = {Naieme Hazrati and Mehdi Elahi},

url = {https://onlinelibrary.wiley.com/doi/epdf/10.1111/exsy.12645},

doi = {https://doi.org/10.1111/exsy.12645},

year  = {2020},

date = {2020-10-19},

journal = {Expert Systems},

volume = {e12645},

pages = {1-20},

abstract = {Over the past years, the research of video recommender systems (RSs) has been mainly focussed on the development of novel algorithms. Although beneficial, still any algorithm may fail to recommend video items that the system has no form of data associated to them (New Item Cold Start). This problem occurs when a new item is added to the catalogue of the system and no data are available for that item. In content‐based RSs, the video items are typically represented by semantic attributes, when generating recommendations. These attributes require a group of experts or users for annotation, and still, the generated recommendations might not capture a complete picture of the users' preferences, for example, the visual tastes of users on video style. This article addresses this problem by proposing recommendation based on novel visual features that do not require human annotation and can represent visual aspects of video items. We have designed a novel evaluation methodology considering three realistic scenarios, that is, (a) extreme cold start, (b) moderate cold start and (c) warm‐start scenario. We have conducted a set of comprehensive experiments, and our results have shown the superior performance of recommendations based on visual features, in all of the evaluation scenarios.},

note = {Pre SFI},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Nunes2020,

title = {A Systematic Review and Taxonomy of Explanations in Decision Support and Recommender Systems},

author = {Ingrid Nunes and Dietmar Jannach},

url = {https://arxiv.org/pdf/2006.08672.pdf},

doi = {10.1007/s11257-017-9195-0},

year  = {2020},

date = {2020-06-15},

journal = {User-Modeling and User-Adapted Interaction},

volume = {27},

number = {3-5},

pages = {393-444},

abstract = {With the recent advances in the field of artificial intelligence, an increasing number of decision-making tasks are delegated to software systems. A key requirement for the success and adoption of such systems is that users must trust system choices or even fully automated decisions. To achieve this, explanation facilities have been widely investigated as a means of establishing trust in these systems since the early years of expert systems. With today's increasingly sophisticated machine learning algorithms, new challenges in the context of explanations, accountability, and trust towards such systems constantly arise. In this work, we systematically review the literature on explanations in advice-giving systems. This is a family of systems that includes recommender systems, which is one of the most successful classes of advice-giving software in practice. We investigate the purposes of explanations as well as how they are generated, presented to users, and evaluated. As a result, we derive a novel comprehensive taxonomy of aspects to be considered when designing explanation facilities for current and future decision support systems. The taxonomy includes a variety of different facets, such as explanation objective, responsiveness, content and presentation. Moreover, we identified several challenges that remain unaddressed so far, for example related to fine-grained issues associated with the presentation of explanations and how explanation facilities are evaluated.},

note = {Pre SFI},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Sjøvaag2020,

title = {Strategic and Organisational fit in Corporate News Markets: A Principal-agent Approach to Studying Newspaper Mergers},

author = {Helle Sjøvaag and Thomas Owren and Turid Borgen},

url = {https://www.tandfonline.com/doi/epub/10.1080/17512786.2020.1772097?needAccess=true},

doi = {https://doi.org/10.1080/17512786.2020.1772097},

year  = {2020},

date = {2020-05-20},

journal = {Journalism Practice},

pages = {1-18},

abstract = {This article analyses strategic and organisational fit in corporate newspaper mergers in the context of the digitalisation of local newspaper markets. Using the 2019 acquisition of Nordsjø Media by Amedia in Norway as case, we analyse how eight editors-in-chief perceive the process of incorporating small, low-frequency, print-oriented monopolistic newspapers into one of Scandinavia’s largest newspaper chains. The semi-structured interviews were analysed in light of perceived strategic and organisational fit in a principal-agent theoretical framework, the aim of which is to shed light on corporate ownership effects in consolidated newspaper markets. The analysis reveals the precarity of independent ownership in digitising news markets, to which corporatisation emerges as a necessary and welcomed solution. We find the strategic fit as perceived by editors to be tied to technological resources and scale economics, while organisational fit is hampered by the speed and pressure of corporatisation processes. While these results largely support findings from previous acquisition studies in the news industries, the contribution of this analysis lies primarily with the necessity of scale required by the technological transformation that forces independent newspapers to submit to larger chain operations and how it influences considerations of fit in disruptive digital news markets.},

note = {Pre SFI},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Zhang2020,

title = {Web Table Extraction, Retrieval, and Augmentation: A Survey},

author = {Shuo Zhang and Krisztian Balog},

url = {https://arxiv.org/pdf/2002.00207.pdf},

year  = {2020},

date = {2020-02-01},

journal = {ACM Transactions on Intelligent Systems and Technology (TIST)},

volume = {11},

number = {2},

pages = {1-35},

abstract = {Tables are a powerful and popular tool for organizing and manipulating data. A vast number of tables can be found on the Web, which represents a valuable knowledge resource. The objective of this survey is to synthesize and present two decades of research on web tables. In particular, we organize existing literature into six main categories of information access tasks: table extraction, table interpretation, table search, question answering, knowledge base augmentation, and table augmentation. For each of these tasks, we identify and describe seminal approaches, present relevant resources, and point out interdependencies among the different tasks.},

note = {Pre SFI},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Sjøvaag2018c,

title = {Is public service broadcasting a threat to commercial media?},

author = {Helle Sjøvaag and Truls André Pedersen and Thomas Owren},

url = {https://journals.sagepub.com/doi/pdf/10.1177/0163443718818354},

doi = {https://doi.org/10.1177/0163443718818354},

year  = {2018},

date = {2018-12-20},

journal = {Media, Culture & Society},

volume = {46},

number = {1},

pages = {808-827},

abstract = {This article asks to what extent public service broadcasting’s online news service resembles that of commercial media. The context of this inquiry is claims of ‘out-crowding’ facing public service broadcasters across Europe. In Norway, commercial players in this debate accuse the public service broadcaster, NRK, of being too similar to competitors in the private sector for commercial operators to attain sustainable revenues in the online realm. To ascertain the extent to what these claims are warranted, this article compares NRK’s online content with that of nine commercial competitors in national and local markets, using a hybrid methodological approach combining quantitative content analysis with Latent Dirichlet allocation, analysing in excess of 115,000 documents. Findings show that commercial operators resemble each other more than they do NRK, indicating closer competition in the commercial segment than between the public service broadcaster and market players.},

note = {Pre SFI},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Sjøvaag2018b,

title = {Female voices in the news: Structural conditions of gender representations in Norwegian newspapers},

author = {Helle Sjøvaag and Truls André Pedersen},

url = {https://journals.sagepub.com/doi/pdf/10.1177/1077699018789885},

doi = {https://doi.org/10.1177/1077699018789885},

year  = {2018},

date = {2018-08-21},

journal = {Journalism & Mass Communication Quarterly},

volume = {96},

number = {1},

pages = {215-238},

abstract = {The article presents a hybrid analysis combining manual content analysis of 9,131 sources in 5,544 news stories across 75 publications, with computational gender recognition producing 551,102 names from 320,228 articles across 125 newspapers. The article investigates the significance of structural features for the presence of women in the news. Results show female sources are only equal to men as ordinary citizens and children, and only in lifestyle content. Among the structural features examined, only local distribution and a circulation less than 5,000 exhibit improvements in female representation. Ownership, distribution frequency, market position, and direct press support had little or no effect.},

note = {Pre SFI},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Sjøvaag2018,

title = {The hyperlinked Scandinavian news ecology: The unequal terms forged by the structural properties of digitalisation},

author = {Helle Sjøvaag and Eirik Stavelin and Michael Karlsson and Aske Kammer},

url = {https://www.tandfonline.com/doi/epub/10.1080/21670811.2018.1454335?needAccess=true},

doi = {https://doi.org/10.1080/21670811.2018.1454335},

year  = {2018},

date = {2018-04-06},

journal = {Digital Journalism},

volume = {7},

number = {4},

pages = {507-531},

abstract = {The article presents a network analysis of 22,861,013 geocoded external hyperlinks, collected from 230 Danish, 220 Norwegian and 208 Swedish news websites in 2016. The analysis asks what the structural properties of the Scandinavian media systems—including its geography and ownership structures—mean for news outlets’ centrality within the hyperlinked news ecology. The analysis finds that whereas incumbent legacy media occupy central positions, about one third of the network is absent from the hyperlinked interaction, primarily local, independently owned newspapers. A multiple linear regression analysis shows that national distribution and corporate ownership correlates to network centrality more than other predictors. As brokers in the network consist of the large, legacy, capital-based news organisations, hyperlink connectivity is primarily characterised by proximity to the centres of power, corporate ownership, agenda setting incumbency and national distribution.},

note = {Pre SFI},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Ludewig2018,

title = {Evaluation of Session-based Recommendation Algorithms},

author = {Malte Ludewig and Dietmar Jannach},

url = {https://arxiv.org/pdf/1803.09587.pdf},

doi = {10.1007/s11257-018-9209-6},

year  = {2018},

date = {2018-03-26},

journal = {User-Modeling and User-Adapted Interaction},

volume = {28},

number = {4-5},

pages = {331-390},

abstract = {Recommender systems help users find relevant items of interest, for example on e-commerce or media streaming sites. Most academic research is concerned with approaches that personalize the recommendations according to long-term user profiles. In many real-world applications, however, such long-term profiles often do not exist and recommendations therefore have to be made solely based on the observed behavior of a user during an ongoing session. Given the high practical relevance of the problem, an increased interest in this problem can be observed in recent years, leading to a number of proposals for session-based recommendation algorithms that typically aim to predict the user's immediate next actions. In this work, we present the results of an in-depth performance comparison of a number of such algorithms, using a variety of datasets and evaluation measures. Our comparison includes the most recent approaches based on recurrent neural networks like GRU4REC, factorized Markov model approaches such as FISM or FOSSIL, as well as simpler methods based, e.g., on nearest neighbor schemes. Our experiments reveal that algorithms of this latter class, despite their sometimes almost trivial nature, often perform equally well or significantly better than today's more complex approaches based on deep neural networks. Our results therefore suggest that there is substantial room for improvement regarding the development of more sophisticated session-based recommendation algorithms.},

note = {Pre SFI},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Quadrana2018,

title = {Sequence-Aware Recommender Systems},

author = {Massimo Quadrana and Paolo Cremonesi and Dietmar Jannach},

url = {https://arxiv.org/pdf/1802.08452.pdf},

year  = {2018},

date = {2018-02-23},

journal = {ACM Computing Surveys},

volume = {51},

number = {4},

pages = {1-35},

abstract = {Recommender systems are one of the most successful applications of data mining and machine learning technology in practice. Academic research in the field is historically often based on the matrix completion problem formulation, where for each user-item-pair only one interaction (e.g., a rating) is considered. In many application domains, however, multiple user-item interactions of different types can be recorded over time. And, a number of recent works have shown that this information can be used to build richer individual user models and to discover additional behavioral patterns that can be leveraged in the recommendation process. In this work we review existing works that consider information from such sequentially-ordered user- item interaction logs in the recommendation process. Based on this review, we propose a categorization of the corresponding recommendation tasks and goals, summarize existing algorithmic solutions, discuss methodological approaches when benchmarking what we call sequence-aware recommender systems, and outline open challenges in the area.},

note = {Pre SFI},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Jannach2016,

title = {Recommender Systems - Beyond Matrix Completion},

author = {Dietmar Jannach and Alexander Tuzhilin and Markus Zanker },

url = {https://www.researchgate.net/publication/309600906_Recommender_systems---_beyond_matrix_completion},

doi = {10.1145/2891406},

year  = {2016},

date = {2016-10-01},

journal = {Communications of the ACM},

volume = {59},

number = {11},

pages = {94-102},

abstract = {Recommender systems have become a natural part of the user experience in today's online world. These systems are able to deliver value both for users and providers and are one prominent example where the output of academic research has a direct impact on the advancements in industry. In this article, we have briefy reviewed the history of this multidis-ciplinary field and looked at recent efforts in the research community to consider the variety of factors that may influence the long-term success of a recommender system. The list of open issues and success factors is still far from complete and new challenges arise constantly that require further research. For example, the huge amounts of user data and preference signals that become available through the Social Web and the Internet of Things not only leads to technical challenges such as scalability, but also to societal questions concerning user privacy. Based on our reflections on the developments in the field, we finally emphasize the need for a more holistic research approach that combines the insights of different disciplines. We urge that research focuses even more on practical problems that matter and are truly suited to increase the utility of recommendations from the viewpoint of the users.

},

note = {Pre SFI},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Elahi2016,

title = {A survey of active learning in collaborative filtering recommender systems},

author = {Mehdi Elahi and Francesco Ricci and Neil Rubens},

url = {https://reader.elsevier.com/reader/sd/pii/S1574013715300150?token=EA12A462FC07F42733F4F13375217A57D3FDC7F6047C133156CB1F4E4487DF24C5366547DF4530A25942F690233F2E30},

doi = {10.1016/j.cosrev.2016.05.002},

year  = {2016},

date = {2016-06-02},

journal = {Computer Science Review},

volume = {20},

pages = {29-50},

abstract = {In collaborative filtering recommender systems user’s preferences are expressed as ratings for items, and each additional rating extends the knowledge of the system and affects the system’s recommendation accuracy. In general, the more ratings are elicited from the users, the more effective the recommendations are. However, the usefulness of each rating may vary significantly, i.e., different ratings may bring a different amount and type of information about the user’s tastes. Hence, specific techniques, which are defined as “active learning strategies”, can be used to selectively choose the items to be presented to the user for rating. In fact, an active learning strategy identifies and adopts criteria for obtaining data that better reflects users’ preferences and enables to generate better recommendations.



So far, a variety of active learning strategies have been proposed in the literature. In this article, we survey recent strategies by grouping them with respect to two distinct dimensions: personalization, i.e., whether the system selected items are different for different users or not, and, hybridization, i.e., whether active learning is guided by a single criterion (heuristic) or by multiple criteria. In addition, we present a comprehensive overview of the evaluation methods and metrics that have been employed by the research community in order to test active learning strategies for collaborative filtering. Finally, we compare the surveyed strategies and provide guidelines for their usage in recommender systems.},

note = {Pre SFI},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Tobias2016,

title = {Alleviating the new user problem in collaborative filtering by exploiting personality information},

author = {Ignacio Fernandez Tobias and Matthias Braunhofer and Mehdi Elahi and Francesco Ricci and Ivan Cantador},

url = {https://www.researchgate.net/publication/285574429_Alleviating_the_New_User_Problem_in_Collaborative_Filtering_by_Exploiting_Personality_Information},

doi = {10.1007/s11257-016-9172-z},

year  = {2016},

date = {2016-06-01},

journal = {User Modeling and User-Adapted Interaction},

volume = {26},

number = {2-3},

pages = {221-255},

abstract = {The new user problem in recommender systems is still challenging, and there is not yet a unique solution that can be applied in any domain or situation. In this paper we analyze viable solutions to the new user problem in collaborative filtering (CF) that are based on the exploitation of user personality information: (a) personality-based CF, which directly improves the recommendation prediction model by incorporating user personality information, (b) personality-based active learning, which utilizes personality information for identifying additional useful preference data in the target recommendation domain to be elicited from the user, and (c) personality-based cross-domain recommendation, which exploits personality information to better use user preference data from auxiliary domains which can be used to compensate the lack of user preference data in the target domain. We benchmark the effectiveness of these methods on large datasets that span several domains, namely movies, music and books. Our results show that personality-aware methods achieve performance improvements that range from 6 to 94 % for users completely new to the system, while increasing the novelty of the recommended items by 3–40 % with respect to the non-personalized popularity baseline. We also discuss the limitations of our approach and the situations in which the proposed methods can be better applied, hence providing guidelines for researchers and practitioners in the field.},

note = {Pre SFI},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Elahi2022,

title = {Developing and Evaluating a University Recommender System},

author = {Mehdi Elahi and Alain D. Starke and Nabil El Ioini and Anna Alexander Lambrix and Christoph Trattner},

url = {https://www.frontiersin.org/articles/10.3389/frai.2021.796268/full},

doi = {https://doi.org/10.3389/frai.2021.796268},

year  = {2022},

date = {2022-02-02},

journal = {Frontiers in Artificial Intelligence },

abstract = {A challenge for many young adults is to find the right institution to follow higher education. Global university rankings are a commonly used, but inefficient tool, for they do not consider a person's preferences and needs. For example, some persons pursue prestige in their higher education, while others prefer proximity. This paper develops and evaluates a university recommender system, eliciting user preferences as ratings to build predictive models and to generate personalized university ranking lists. In Study 1, we performed offline evaluation on a rating dataset to determine which recommender approaches had the highest predictive value. In Study 2, we selected three algorithms to produce different university recommendation lists in our online tool, asking our users to compare and evaluate them in terms of different metrics (Accuracy, Diversity, Perceived Personalization, Satisfaction, and Novelty). We show that a SVD algorithm scores high on accuracy and perceived personalization, while a KNN algorithm scores better on novelty. We also report findings on preferred university features.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Starke2021,

title = {Nudging Healthy Choices in Food Search Through Visual Attractiveness},

author = {Alain D. Starke and Martijn C. Willemsen and Christoph Trattner},

url = {https://www.frontiersin.org/articles/10.3389/frai.2021.621743/full},

doi = {10.3389/frai.2021.621743},

year  = {2021},

date = {2021-04-22},

number = {April 2021},

pages = {1-18},

abstract = {Recipe websites are becoming increasingly popular to support people in their home cooking. However, most of these websites prioritize popular recipes, which tend to be unhealthy. Drawing upon research on visual biases and nudges, this paper investigates whether healthy food choices can be supported in food search by depicting attractive images alongside recipes, as well as by re-ranking search results on health. After modelling the visual attractiveness of recipe images, we asked 239 users to search for specific online recipes and to select those they liked the most. Our analyses revealed that users tended to choose a healthier recipe if a visually attractive image was depicted alongside it, as well as if it was listed at the top of a list of search results. Even though less popular recipes were promoted this way, it did not come at the cost of a user’s level of satisfaction},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Endriss2020,

title = {The complexity landscape of outcome determination in judgment aggregation},

author = {Ulle Endriss and Ronald de Haan and Jerôme Lang and Marija Slavkovik },

url = {https://www.jair.org/index.php/jair/article/view/11970/26619},

doi = {10.1613/jair.1.11970},

year  = {2020},

date = {2020-11-04},

journal = {Journal of Artificial Intelligence Research},

volume = {69},

pages = {687–731},

abstract = {We provide a comprehensive analysis of the computational complexity of the outcome determinationproblem for the most important aggregation rules proposed in the literature on logic-based judgmentaggregation. Judgment aggregation is a powerful and flexible framework for studying problems ofcollective decision making that has attracted interest in a range of disciplines, including Legal Theory,Philosophy, Economics, Political Science, and Artificial Intelligence. The problem of computing theoutcome for a given list of individual judgments to be aggregated into a single collective judgment isthe most fundamental algorithmic challenge arising in this context. Our analysis applies to severaldifferent variants of the basic framework of judgment aggregation that have been discussed in theliterature, as well as to a new framework that encompasses all existing such frameworks in terms ofexpressive power and representational succinctness.},

note = {Pre SFI},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Hazrati2020,

title = { Addressing the New Item problem in video recommender systems by incorporation of visual features with restricted Boltzmann machines.},

author = {Naieme Hazrati and Mehdi Elahi},

url = {https://onlinelibrary.wiley.com/doi/epdf/10.1111/exsy.12645},

doi = {https://doi.org/10.1111/exsy.12645},

year  = {2020},

date = {2020-10-19},

journal = {Expert Systems},

volume = {e12645},

pages = {1-20},

abstract = {Over the past years, the research of video recommender systems (RSs) has been mainly focussed on the development of novel algorithms. Although beneficial, still any algorithm may fail to recommend video items that the system has no form of data associated to them (New Item Cold Start). This problem occurs when a new item is added to the catalogue of the system and no data are available for that item. In content‐based RSs, the video items are typically represented by semantic attributes, when generating recommendations. These attributes require a group of experts or users for annotation, and still, the generated recommendations might not capture a complete picture of the users' preferences, for example, the visual tastes of users on video style. This article addresses this problem by proposing recommendation based on novel visual features that do not require human annotation and can represent visual aspects of video items. We have designed a novel evaluation methodology considering three realistic scenarios, that is, (a) extreme cold start, (b) moderate cold start and (c) warm‐start scenario. We have conducted a set of comprehensive experiments, and our results have shown the superior performance of recommendations based on visual features, in all of the evaluation scenarios.},

note = {Pre SFI},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Nunes2020,

title = {A Systematic Review and Taxonomy of Explanations in Decision Support and Recommender Systems},

author = {Ingrid Nunes and Dietmar Jannach},

url = {https://arxiv.org/pdf/2006.08672.pdf},

doi = {10.1007/s11257-017-9195-0},

year  = {2020},

date = {2020-06-15},

journal = {User-Modeling and User-Adapted Interaction},

volume = {27},

number = {3-5},

pages = {393-444},

abstract = {With the recent advances in the field of artificial intelligence, an increasing number of decision-making tasks are delegated to software systems. A key requirement for the success and adoption of such systems is that users must trust system choices or even fully automated decisions. To achieve this, explanation facilities have been widely investigated as a means of establishing trust in these systems since the early years of expert systems. With today's increasingly sophisticated machine learning algorithms, new challenges in the context of explanations, accountability, and trust towards such systems constantly arise. In this work, we systematically review the literature on explanations in advice-giving systems. This is a family of systems that includes recommender systems, which is one of the most successful classes of advice-giving software in practice. We investigate the purposes of explanations as well as how they are generated, presented to users, and evaluated. As a result, we derive a novel comprehensive taxonomy of aspects to be considered when designing explanation facilities for current and future decision support systems. The taxonomy includes a variety of different facets, such as explanation objective, responsiveness, content and presentation. Moreover, we identified several challenges that remain unaddressed so far, for example related to fine-grained issues associated with the presentation of explanations and how explanation facilities are evaluated.},

note = {Pre SFI},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Sjøvaag2020,

title = {Strategic and Organisational fit in Corporate News Markets: A Principal-agent Approach to Studying Newspaper Mergers},

author = {Helle Sjøvaag and Thomas Owren and Turid Borgen},

url = {https://www.tandfonline.com/doi/epub/10.1080/17512786.2020.1772097?needAccess=true},

doi = {https://doi.org/10.1080/17512786.2020.1772097},

year  = {2020},

date = {2020-05-20},

journal = {Journalism Practice},

pages = {1-18},

abstract = {This article analyses strategic and organisational fit in corporate newspaper mergers in the context of the digitalisation of local newspaper markets. Using the 2019 acquisition of Nordsjø Media by Amedia in Norway as case, we analyse how eight editors-in-chief perceive the process of incorporating small, low-frequency, print-oriented monopolistic newspapers into one of Scandinavia’s largest newspaper chains. The semi-structured interviews were analysed in light of perceived strategic and organisational fit in a principal-agent theoretical framework, the aim of which is to shed light on corporate ownership effects in consolidated newspaper markets. The analysis reveals the precarity of independent ownership in digitising news markets, to which corporatisation emerges as a necessary and welcomed solution. We find the strategic fit as perceived by editors to be tied to technological resources and scale economics, while organisational fit is hampered by the speed and pressure of corporatisation processes. While these results largely support findings from previous acquisition studies in the news industries, the contribution of this analysis lies primarily with the necessity of scale required by the technological transformation that forces independent newspapers to submit to larger chain operations and how it influences considerations of fit in disruptive digital news markets.},

note = {Pre SFI},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Zhang2020,

title = {Web Table Extraction, Retrieval, and Augmentation: A Survey},

author = {Shuo Zhang and Krisztian Balog},

url = {https://arxiv.org/pdf/2002.00207.pdf},

year  = {2020},

date = {2020-02-01},

journal = {ACM Transactions on Intelligent Systems and Technology (TIST)},

volume = {11},

number = {2},

pages = {1-35},

abstract = {Tables are a powerful and popular tool for organizing and manipulating data. A vast number of tables can be found on the Web, which represents a valuable knowledge resource. The objective of this survey is to synthesize and present two decades of research on web tables. In particular, we organize existing literature into six main categories of information access tasks: table extraction, table interpretation, table search, question answering, knowledge base augmentation, and table augmentation. For each of these tasks, we identify and describe seminal approaches, present relevant resources, and point out interdependencies among the different tasks.},

note = {Pre SFI},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Sjøvaag2018c,

title = {Is public service broadcasting a threat to commercial media?},

author = {Helle Sjøvaag and Truls André Pedersen and Thomas Owren},

url = {https://journals.sagepub.com/doi/pdf/10.1177/0163443718818354},

doi = {https://doi.org/10.1177/0163443718818354},

year  = {2018},

date = {2018-12-20},

journal = {Media, Culture & Society},

volume = {46},

number = {1},

pages = {808-827},

abstract = {This article asks to what extent public service broadcasting’s online news service resembles that of commercial media. The context of this inquiry is claims of ‘out-crowding’ facing public service broadcasters across Europe. In Norway, commercial players in this debate accuse the public service broadcaster, NRK, of being too similar to competitors in the private sector for commercial operators to attain sustainable revenues in the online realm. To ascertain the extent to what these claims are warranted, this article compares NRK’s online content with that of nine commercial competitors in national and local markets, using a hybrid methodological approach combining quantitative content analysis with Latent Dirichlet allocation, analysing in excess of 115,000 documents. Findings show that commercial operators resemble each other more than they do NRK, indicating closer competition in the commercial segment than between the public service broadcaster and market players.},

note = {Pre SFI},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Sjøvaag2018b,

title = {Female voices in the news: Structural conditions of gender representations in Norwegian newspapers},

author = {Helle Sjøvaag and Truls André Pedersen},

url = {https://journals.sagepub.com/doi/pdf/10.1177/1077699018789885},

doi = {https://doi.org/10.1177/1077699018789885},

year  = {2018},

date = {2018-08-21},

journal = {Journalism & Mass Communication Quarterly},

volume = {96},

number = {1},

pages = {215-238},

abstract = {The article presents a hybrid analysis combining manual content analysis of 9,131 sources in 5,544 news stories across 75 publications, with computational gender recognition producing 551,102 names from 320,228 articles across 125 newspapers. The article investigates the significance of structural features for the presence of women in the news. Results show female sources are only equal to men as ordinary citizens and children, and only in lifestyle content. Among the structural features examined, only local distribution and a circulation less than 5,000 exhibit improvements in female representation. Ownership, distribution frequency, market position, and direct press support had little or no effect.},

note = {Pre SFI},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Sjøvaag2018,

title = {The hyperlinked Scandinavian news ecology: The unequal terms forged by the structural properties of digitalisation},

author = {Helle Sjøvaag and Eirik Stavelin and Michael Karlsson and Aske Kammer},

url = {https://www.tandfonline.com/doi/epub/10.1080/21670811.2018.1454335?needAccess=true},

doi = {https://doi.org/10.1080/21670811.2018.1454335},

year  = {2018},

date = {2018-04-06},

journal = {Digital Journalism},

volume = {7},

number = {4},

pages = {507-531},

abstract = {The article presents a network analysis of 22,861,013 geocoded external hyperlinks, collected from 230 Danish, 220 Norwegian and 208 Swedish news websites in 2016. The analysis asks what the structural properties of the Scandinavian media systems—including its geography and ownership structures—mean for news outlets’ centrality within the hyperlinked news ecology. The analysis finds that whereas incumbent legacy media occupy central positions, about one third of the network is absent from the hyperlinked interaction, primarily local, independently owned newspapers. A multiple linear regression analysis shows that national distribution and corporate ownership correlates to network centrality more than other predictors. As brokers in the network consist of the large, legacy, capital-based news organisations, hyperlink connectivity is primarily characterised by proximity to the centres of power, corporate ownership, agenda setting incumbency and national distribution.},

note = {Pre SFI},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Ludewig2018,

title = {Evaluation of Session-based Recommendation Algorithms},

author = {Malte Ludewig and Dietmar Jannach},

url = {https://arxiv.org/pdf/1803.09587.pdf},

doi = {10.1007/s11257-018-9209-6},

year  = {2018},

date = {2018-03-26},

journal = {User-Modeling and User-Adapted Interaction},

volume = {28},

number = {4-5},

pages = {331-390},

abstract = {Recommender systems help users find relevant items of interest, for example on e-commerce or media streaming sites. Most academic research is concerned with approaches that personalize the recommendations according to long-term user profiles. In many real-world applications, however, such long-term profiles often do not exist and recommendations therefore have to be made solely based on the observed behavior of a user during an ongoing session. Given the high practical relevance of the problem, an increased interest in this problem can be observed in recent years, leading to a number of proposals for session-based recommendation algorithms that typically aim to predict the user's immediate next actions. In this work, we present the results of an in-depth performance comparison of a number of such algorithms, using a variety of datasets and evaluation measures. Our comparison includes the most recent approaches based on recurrent neural networks like GRU4REC, factorized Markov model approaches such as FISM or FOSSIL, as well as simpler methods based, e.g., on nearest neighbor schemes. Our experiments reveal that algorithms of this latter class, despite their sometimes almost trivial nature, often perform equally well or significantly better than today's more complex approaches based on deep neural networks. Our results therefore suggest that there is substantial room for improvement regarding the development of more sophisticated session-based recommendation algorithms.},

note = {Pre SFI},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Quadrana2018,

title = {Sequence-Aware Recommender Systems},

author = {Massimo Quadrana and Paolo Cremonesi and Dietmar Jannach},

url = {https://arxiv.org/pdf/1802.08452.pdf},

year  = {2018},

date = {2018-02-23},

journal = {ACM Computing Surveys},

volume = {51},

number = {4},

pages = {1-35},

abstract = {Recommender systems are one of the most successful applications of data mining and machine learning technology in practice. Academic research in the field is historically often based on the matrix completion problem formulation, where for each user-item-pair only one interaction (e.g., a rating) is considered. In many application domains, however, multiple user-item interactions of different types can be recorded over time. And, a number of recent works have shown that this information can be used to build richer individual user models and to discover additional behavioral patterns that can be leveraged in the recommendation process. In this work we review existing works that consider information from such sequentially-ordered user- item interaction logs in the recommendation process. Based on this review, we propose a categorization of the corresponding recommendation tasks and goals, summarize existing algorithmic solutions, discuss methodological approaches when benchmarking what we call sequence-aware recommender systems, and outline open challenges in the area.},

note = {Pre SFI},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Jannach2016,

title = {Recommender Systems - Beyond Matrix Completion},

author = {Dietmar Jannach and Alexander Tuzhilin and Markus Zanker },

url = {https://www.researchgate.net/publication/309600906_Recommender_systems---_beyond_matrix_completion},

doi = {10.1145/2891406},

year  = {2016},

date = {2016-10-01},

journal = {Communications of the ACM},

volume = {59},

number = {11},

pages = {94-102},

abstract = {Recommender systems have become a natural part of the user experience in today's online world. These systems are able to deliver value both for users and providers and are one prominent example where the output of academic research has a direct impact on the advancements in industry. In this article, we have briefy reviewed the history of this multidis-ciplinary field and looked at recent efforts in the research community to consider the variety of factors that may influence the long-term success of a recommender system. The list of open issues and success factors is still far from complete and new challenges arise constantly that require further research. For example, the huge amounts of user data and preference signals that become available through the Social Web and the Internet of Things not only leads to technical challenges such as scalability, but also to societal questions concerning user privacy. Based on our reflections on the developments in the field, we finally emphasize the need for a more holistic research approach that combines the insights of different disciplines. We urge that research focuses even more on practical problems that matter and are truly suited to increase the utility of recommendations from the viewpoint of the users.

},

note = {Pre SFI},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Elahi2016,

title = {A survey of active learning in collaborative filtering recommender systems},

author = {Mehdi Elahi and Francesco Ricci and Neil Rubens},

url = {https://reader.elsevier.com/reader/sd/pii/S1574013715300150?token=EA12A462FC07F42733F4F13375217A57D3FDC7F6047C133156CB1F4E4487DF24C5366547DF4530A25942F690233F2E30},

doi = {10.1016/j.cosrev.2016.05.002},

year  = {2016},

date = {2016-06-02},

journal = {Computer Science Review},

volume = {20},

pages = {29-50},

abstract = {In collaborative filtering recommender systems user’s preferences are expressed as ratings for items, and each additional rating extends the knowledge of the system and affects the system’s recommendation accuracy. In general, the more ratings are elicited from the users, the more effective the recommendations are. However, the usefulness of each rating may vary significantly, i.e., different ratings may bring a different amount and type of information about the user’s tastes. Hence, specific techniques, which are defined as “active learning strategies”, can be used to selectively choose the items to be presented to the user for rating. In fact, an active learning strategy identifies and adopts criteria for obtaining data that better reflects users’ preferences and enables to generate better recommendations.



So far, a variety of active learning strategies have been proposed in the literature. In this article, we survey recent strategies by grouping them with respect to two distinct dimensions: personalization, i.e., whether the system selected items are different for different users or not, and, hybridization, i.e., whether active learning is guided by a single criterion (heuristic) or by multiple criteria. In addition, we present a comprehensive overview of the evaluation methods and metrics that have been employed by the research community in order to test active learning strategies for collaborative filtering. Finally, we compare the surveyed strategies and provide guidelines for their usage in recommender systems.},

note = {Pre SFI},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Tobias2016,

title = {Alleviating the new user problem in collaborative filtering by exploiting personality information},

author = {Ignacio Fernandez Tobias and Matthias Braunhofer and Mehdi Elahi and Francesco Ricci and Ivan Cantador},

url = {https://www.researchgate.net/publication/285574429_Alleviating_the_New_User_Problem_in_Collaborative_Filtering_by_Exploiting_Personality_Information},

doi = {10.1007/s11257-016-9172-z},

year  = {2016},

date = {2016-06-01},

journal = {User Modeling and User-Adapted Interaction},

volume = {26},

number = {2-3},

pages = {221-255},

abstract = {The new user problem in recommender systems is still challenging, and there is not yet a unique solution that can be applied in any domain or situation. In this paper we analyze viable solutions to the new user problem in collaborative filtering (CF) that are based on the exploitation of user personality information: (a) personality-based CF, which directly improves the recommendation prediction model by incorporating user personality information, (b) personality-based active learning, which utilizes personality information for identifying additional useful preference data in the target recommendation domain to be elicited from the user, and (c) personality-based cross-domain recommendation, which exploits personality information to better use user preference data from auxiliary domains which can be used to compensate the lack of user preference data in the target domain. We benchmark the effectiveness of these methods on large datasets that span several domains, namely movies, music and books. Our results show that personality-aware methods achieve performance improvements that range from 6 to 94 % for users completely new to the system, while increasing the novelty of the recommended items by 3–40 % with respect to the non-personalized popularity baseline. We also discuss the limitations of our approach and the situations in which the proposed methods can be better applied, hence providing guidelines for researchers and practitioners in the field.},

note = {Pre SFI},

keywords = {},

pubstate = {published},

tppubtype = {article}

}