@article{Hauptmann2022,

title = {Research directions in recommender systems for health and well-being},

author = {Hanna Hauptmann and Alan Said and Christoph Trattner },

url = {https://mediafutures.no/s11257-022-09349-4/},

year  = {2022},

date = {2022-11-14},

urldate = {2022-11-14},

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

abstract = {Recommender systems have been put to use in the entertainment and e-commerce domains for decades, and in these decades, recommender systems have grown and matured into reliable and ubiquitous systems in today’s digital landscape. Relying on this maturity, the application of recommender systems for health and well-being has seen a rise in recent years, paving the way for tailored and personalized systems that support caretakers, caregivers, and other users in the health domain. In this introduction, we give a brief overview of the stakes, the requirements, and the possibilities that recommender systems for health and well-being bring.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Bogina2022,

title = {Considering Temporal Aspects in Recommender Systems: A Survey },

author = {Veronica Bogina and Tsvi Kuflik and Dietmar Jannach and Maria Bielikova and Michal Kompan and Christoph Trattner 

},

url = {https://mediafutures.no/revisedversion_considering_temporal_aspects_in_rs_a_survey-6/},

year  = {2022},

date = {2022-05-31},

urldate = {2022-05-31},

journal = {UMUAI journal},

abstract = {The widespread use of temporal aspects in user modeling indicates their importance, and their consideration showed to be highly effective in var- ious domains related to user modeling, especially in recommender systems. Still, past and ongoing research, spread over several decades, provided multi- ple ad-hoc solutions, but no common understanding of the issue. There is no standardization and there is often little commonality in considering tempo- ral aspects in different applications. This may ultimately lead to the problem that application developers define ad-hoc solutions for their problems at hand, sometimes missing or neglecting aspects that proved to be effective in similar cases. Therefore, a comprehensive survey of the consideration of temporal as- pects in recommender systems is required. In this work, we provide an overview of various time-related aspects, categorize existing research, present a tempo- ral abstraction and point to gaps that require future research. We anticipate this survey will become a reference point for researchers and practitioners alike when considering the potential application of temporal aspects in their personalized applications.},

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{cristin2000622,

title = {Responsible media technology and AI: challenges and research directions},

author = {Christoph Trattner and Dietmar Jannach and Enrico Motta and Irene Costera Meijer and Nicholas Diakopoulos and Mehdi Elahi and Andreas Lothe Opdahl and Bjørnar Tessem and Njål Trygve Borch and Morten Fjeld and Lilja Øvrelid and Koenraad De Smedt and Hallvard Moe},

url = {https://app.cristin.no/results/show.jsf?id=2000622, Cristin

https://link.springer.com/content/pdf/10.1007/s43681-021-00126-4.pdf},

doi = {https://doi.org/10.1007/s43681-021-00126-4},

year  = {2021},

date = {2021-12-20},

journal = {AI and Ethics},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{cristin1953352,

title = {Towards Responsible Media Recommendation},

author = {Mehdi Elahi and Dietmar Jannach and Lars Skjærven and Erik Knudsen and Helle Sjøvaag and Kristian Tolonen and Øyvind Holmstad and Igor Pipkin and Eivind Throndsen and Agnes Stenbom and Eivind Fiskerud and Adrian Oesch and Loek Vredenberg and Christoph Trattner},

url = {https://app.cristin.no/results/show.jsf?id=1953352, Cristin

https://link.springer.com/article/10.1007%2Fs43681-021-00107-7},

doi = {https://doi.org/10.1007/s43681-021-00107-7},

year  = {2021},

date = {2021-11-02},

journal = {AI and Ethics},

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{cristin1933059,

title = {Changing Salty Food Preferences with Visual and Textual

Explanations in a Search Interface},

author = {Arngeir Berge and Vegard Velle Sjøen and Alain Starke and Christoph Trattner},

url = {https://app.cristin.no/results/show.jsf?id=1933059, Cristin

http://ceur-ws.org/Vol-2903/IUI21WS-HEALTHI-2.pdf},

year  = {2021},

date = {2021-01-01},

journal = {CEUR Workshop Proceedings},

abstract = {Salt is consumed at too high levels in the general population, causing high blood pressure and related health problems. In this paper, we present results of ongoing research that tries to reduce salt intake via technology and in particular from an interface perspective. In detail, this paper features results of a study that examines the extent to which visual and textual explanations in a search interface can change salty food preferences. An online user study with 200 participants demonstrates that this is possible in food search results by accompanying recipes with a visual taste map that includes salt-replacer herbs and spices in the calculation of salty taste.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Trattner2020,

title = {Learning to Recommend Similar Items from Human Judgements},

author = {Christoph Trattner and Dietmar Jannach},

url = {https://www.christophtrattner.info/pubs/UMUAI2019.pdf},

doi = {10.1007/s11257-019-09245-4},

year  = {2019},

date = {2019-09-20},

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

pages = {1-50},

abstract = {Similar item recommendations—a common feature of many Web sites—point users to other interesting objects given a currently inspected item. A common way of computing such recommendations is to use a similarity function, which expresses how much alike two given objects are. Such similarity functions are usually designed based on the specifics of the given application domain. In this work, we explore how such functions can be learned from human judgments of similarities between objects, using two domains of “quality and taste”—cooking recipe and movie recommendation—as guiding scenarios. In our approach, we first collect a few thousand pairwise similarity assessments with the help of crowdworkers. Using these data, we then train different machine learning models that can be used as similarity functions to compare objects. Offline analyses reveal for both application domains that models that combine different types of item characteristics are the best predictors for human-perceived similarity. To further validate the usefulness of the learned models, we conducted additional user studies. In these studies, we exposed participants to similar item recommendations using a set of models that were trained with different feature subsets. The results showed that the combined models that exhibited the best offline prediction performance led to the highest user-perceived similarity, but also to recommendations that were considered useful by the participants, thus confirming the feasibility of our approach.},

note = {Pre SFI},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Sciascio2018,

title = {The Roadmap to User-Controllable Social Exploratory Search},

author = {Cecilia Di Sciascio and Peter Brusilovsky and Christoph Trattner and Eduardo Veas},

url = {https://www.christophtrattner.info/pubs/TIIS2019.pdf},

year  = {2018},

date = {2018-01-01},

journal = {ACM Transactions on Interactive Intelligent Systems},

pages = {1-37},

abstract = {Information-seeking tasks with learning or investigative purposes are usually referred to as exploratory

search. Exploratory search unfolds as a dynamic process where the user, amidst navigation, trial-and-error

and on-the-!y selections, gathers and organizes information (resources). A range of innovative interfaces

with increased user control have been developed to support exploratory search process. In this work we

present our attempt to increase the power of exploratory search interfaces by using ideas of social search,

i.e., leveraging information left by past users of information systems. Social search technologies are highly

popular nowadays, especially for improving ranking. However, current approaches to social ranking do not

allow users to decide to what extent social information should be taken into account for result ranking. This

paper presents an interface that integrates social search functionality into an exploratory search system in

a user-controlled way that is consistent with the nature of exploratory search. The interface incorporates

control features that allow the user to (i) express information needs by selecting keywords and (ii) to express

preferences for incorporating social wisdom based on tag matching and user similarity. The interface promotes

search transparency through color-coded stacked bars and rich tooltips. This work presents the full series

of evaluations conducted to, "rst, assess the value of the social models in contexts independent to the user

interface, in terms of objective and perceived accuracy. Then, in a study with the full-!edged system, we

investigated system accuracy and subjective aspects with a structural model that revealed that, when users

actively interacted with all its control features, the hybrid system outperformed a baseline content-based-only

tool and users were more satis"ed.},

note = {Pre SFI},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Mutlu2016,

title = {VizRec: Recommending Personalized Visualizations},

author = {Belgin Mutlu and Eduardo Veas and Christoph Trattner},

url = {https://www.christophtrattner.info/pubs/ACM-TIIS.pdf},

year  = {2016},

date = {2016-01-01},

journal = {ACM Transactions on Interactive Intelligent Systems (TiiS)},

volume = {6},

number = {4},

pages = {1-40},

abstract = {Visualizations have a distinctive advantage when dealing with the information overload problem: since they

are grounded in basic visual cognition, many people understand them. However, creating the appropriate

representation requires specific expertise of the domain and underlying data. Our quest in this paper is to

study methods to suggest appropriate visualizations autonomously. To be appropriate, a visualization has

to follow studied guidelines to find and distinguish patterns visually, and encode data therein. Thus, a 

visualization tells a story of the underlying data; yet, to be appropriate, it has to clearly represent those aspects

of the data the viewer is interested in. Which aspects of a visualization are important to the viewer? Can

we capture and use those aspects to recommend visualizations? This paper investigates strategies to

 recommend visualizations considering different aspects of user preferences. A multi-dimensional scale is used to

estimate aspects of quality for charts for collaborative filtering. Alternatively, tag vectors describing charts

are used to recommend potentially interesting charts based on content. Finally, a hybrid approach combines

information on what a chart is about (tags) and how good it is (ratings). We present the design principles

behind VizRec, our visual recommender. We describe its architecture, the data acquisition approach with a

crowd sourced study, and the analysis of strategies for visualization recommendation.},

note = {Pre SFI},

keywords = {},

pubstate = {published},

tppubtype = {article}

}