@conference{deHaan2019,

title = {Answer set programming for judgment aggregation},

author = {Ronald de Haan and Marija Slavkovik},

url = {https://www.ijcai.org/Proceedings/2019/0231.pdf},

year  = {2019},

date = {2019-08-10},

booktitle = {Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence},

pages = {1668-1674},

publisher = {International Joint Conferences on Artificial Intelligence},

abstract = {Judgment aggregation (JA) studies how to aggregate truth valuations on logically related issues. Computing the outcome of aggregation procedures is notoriously computationally hard, which is the likely reason that no implementation of them exists as of yet. However, even hard problems sometimes need to be solved. The worst-case computational complexity of answer set programming (ASP) matches that of most problems in judgment aggregation. We take advantage of this and propose a natural and modular encoding of various judgment aggregation procedures and related problems in JA into ASP. With these encodings, we achieve two results: (1) paving the way towards constructing a wide range of new benchmark instances (from JA) for answer set solving algorithms; and (2) providing an automated tool for researchers in the area of judgment aggregation.},

note = {Pre SFI},

keywords = {WP2: User Modeling Personalization and Engagement},

pubstate = {published},

tppubtype = {conference}

}

@conference{Balog2019,

title = {Transparent, Scrutable and Explainable User Models for Personalized Recommendation},

author = {Krisztian Balog and Filip Radlinski and Shushan Arakelyan},

url = {https://dl.acm.org/doi/pdf/10.1145/3331184.3331211},

doi = {https://doi.org/10.1145/3331184.3331211},

year  = {2019},

date = {2019-07-25},

booktitle = {Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’19)},

pages = {265–274},

abstract = {Most recommender systems base their recommendations on implicit or explicit item-level feedback provided by users. These item ratings are combined into a complex user model, which then predicts the suitability of other items. While effective, such methods have limited scrutability and transparency. For instance, if a user's interests change, then many item ratings would usually need to be modified to significantly shift the user's recommendations. Similarly, explaining how the system characterizes the user is impossible, short of presenting the entire list of known item ratings. In this paper, we present a new set-based recommendation technique that permits the user model to be explicitly presented to users in natural language, empowering users to understand recommendations made and improve the recommendations dynamically. While performing comparably to traditional collaborative filtering techniques in a standard static setting, our approach allows users to efficiently improve recommendations. Further, it makes it easier for the model to be validated and adjusted, building user trust and understanding.},

note = {Pre SFI},

keywords = {Explainability, Recommendations, Scrutability, Transparency, WP2: User Modeling Personalization and Engagement},

pubstate = {published},

tppubtype = {conference}

}

@conference{Tessem2019,

title = {Supporting Journalistic News Angles with Models and Analogies},

author = {Bjørnar Tessem and Andreas L. Opdahl},

url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8877058},

doi = {10.1109/RCIS.2019.8877058},

year  = {2019},

date = {2019-05-31},

urldate = {2019-05-31},

booktitle = { 2019 13th International Conference on Research Challenges in Information Science (RCIS)},

pages = {1-7},

abstract = {News angles are approaches to content presentation in journalism, where the journalist chooses which facts of an event to present. The News Angler project investigates how to computationally support the creation and selection of original news angles for a news event based on information from big data sources. At least two creative approaches are possible. One is to maintain a library of well-known news angles represented in a suitable modeling language, matching published reports on a current event to news angles in order to identify possible angles that have not yet been used. A second approach is not to represent news angles explicitly, instead matching the current event with previous events, and transferring angles from past to present reports by similarity and analogy. Both approaches are described and technologies needed to proceed in either direction are discussed.},

note = {Pre SFI},

keywords = {WP3: Media Content Production and Analysis},

pubstate = {published},

tppubtype = {conference}

}

@conference{Flobak2019,

title = {Participatory Design of VR Scenarios for Exposure Therapy},

author = {Eivind Flobak and Jo Dugstad Wake and Joakim Vindenes and Smiti Kahlon and T. Nordgreen and Frode Guribye},

url = {https://www.researchgate.net/publication/330205387_Participatory_Design_of_VR_Scenarios_for_Exposure_Therapy},

doi = {10.1145/3290605.3300799 },

year  = {2019},

date = {2019-05-01},

booktitle = {Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI '19)},

number = {Paper 569},

address = {New York},

abstract = {Virtual reality (VR) applications for exposure therapy predominantly use computer-generated imagery to create controlled environments in which users can be exposed to their fears. Creating 3D animations, however, is demanding and time-consuming. This paper presents a participatory approach for prototyping VR scenarios that are enabled by 360° video and grounded in lived experiences. We organized a participa-tory workshop with adolescents to prototype such scenarios, consisting of iterative phases of ideation, storyboarding, live-action plays recorded by a 360° camera, and group evaluation. Through an analysis of the participants' interactions, we outline how they worked to design prototypes that depict situations relevant to those with a fear of public speaking. Our analysis also explores how participants used their experiences and refections as resources for design. Six clinical psychologists evaluated the prototypes from the workshop and concluded they were viable therapeutic tools, emphasizing the immer-sive, realistic experience they presented. We argue that our approach makes the design of VR scenarios more accessible.},

note = {Pre SFI},

keywords = {WP4: Media Content Interaction and Accessibility},

pubstate = {published},

tppubtype = {conference}

}

@conference{Liao2019,

title = {Building Jiminy Cricket: An architecture for moral agreements among stakeholders},

author = {Beishui Liao and Marija Slavkovik and Leendert van der Torre},

url = {https://arxiv.org/pdf/1812.04741.pdf},

year  = {2019},

date = {2019-03-07},

booktitle = {Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society},

pages = {147–153},

abstract = {An autonomous system is constructed by a manufacturer, operates in a society subject to norms and laws, and is interacting with end-users. We address the

challenge of how the moral values and views of all stakeholders can be integrated

and reflected in the moral behaviour of the autonomous system. We propose an artificial moral agent architecture that uses techniques from normative systems and

formal argumentation to reach moral agreements among stakeholders. We show

how our architecture can be used not only for ethical practical reasoning and collaborative decision-making, but also for the explanation of such moral behavior.},

note = {Pre SFI},

keywords = {Artificial Intelligence, WP2: User Modeling Personalization and Engagement},

pubstate = {published},

tppubtype = {conference}

}

@conference{Dyrkolbotn2018,

title = {On the distinction between implicit and explicit ethical agency},

author = {Sjur Dyrkolbotn and Truls Pedersen and Marija Slavkovik},

url = {https://dl.acm.org/doi/pdf/10.1145/3278721.3278769},

doi = {https://doi.org/10.1145/3278721.3278769},

year  = {2018},

date = {2018-12-01},

booktitle = {Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society (AIES '18)},

pages = {74–80},

abstract = {With recent advances in artificial intelligence and the rapidly increasing importance of autonomous intelligent systems in society, it is becoming clear that artificial agents will have to be designed to comply with complex ethical standards. As we work to develop moral machines, we also push the boundaries of existing legal categories. The most pressing question is what kind of ethical decision-making our machines are actually able to engage in. Both in law and in ethics, the concept of agency forms a basis for further legal and ethical categorisations, pertaining to decision-making ability. Hence, without a cross-disciplinary understanding of what we mean by ethical agency in machines, the question of responsibility and liability cannot be clearly addressed. Here we make first steps towards a comprehensive definition, by suggesting ways to distinguish between implicit and explicit forms of ethical agency.},

note = {Pre SFI},

keywords = {Agency, Artificial Intelligence, Autonomy, Epistemology, Ethics, WP2: User Modeling Personalization and Engagement},

pubstate = {published},

tppubtype = {conference}

}

@conference{Nguyen2018,

title = {Challenges and opportunities within personal life archives},

author = {Duc-Tien Dang-Nguyen and Michael Alexander Riegler and Liting Zhou and Cathal Gurrin},

url = {https://www.researchgate.net/publication/325706506_Challenges_and_Opportunities_within_Personal_Life_Archives},

doi = {10.1145/3206025.3206040},

year  = {2018},

date = {2018-06-01},

urldate = {2018-06-01},

booktitle = {Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval},

pages = {335-343},

abstract = {Nowadays, almost everyone holds some form or other of a personal life archive. Automatically maintaining such an archive is an activity that is becoming increasingly common, however without automatic support the users will quickly be overwhelmed by the volume of data and will miss out on the potential benefits that lifelogs provide. In this paper we give an overview of the current status of lifelog research and propose a concept for exploring these archives. We motivate the need for new methodologies for indexing data, organizing content and supporting information access. Finally we will describe challenges to be addressed and give an overview of initial steps that have to be taken, to address the challenges of organising and searching personal life archives.},

note = {Pre SFI},

keywords = {Lifelogging, Personal Life Archiv, Search Engine, WP3: Media Content Production and Analysis},

pubstate = {published},

tppubtype = {conference}

}

@conference{Setty2018,

title = {Neural embeddings for news events},

author = {Vinay Setty and Katja Hose},

url = {https://dl.acm.org/doi/pdf/10.1145/3209978.3210136},

doi = {10.1145/3209978.3210136},

year  = {2018},

date = {2018-06-01},

booktitle = {The 41st international acm sigir conference on research development in information retrieval},

pages = {1013–1016},

publisher = {Association for Computing Machinery},

address = {New York},

organization = {Association for Computing Machinery},

abstract = {Representation of news events as latent feature vectors is essential for several tasks, such as news recommendation, news event linking, etc. However, representations proposed in the past fail to capture the complex network structure of news events. In this paper we propose Event2Vec, a novel way to learn latent feature vectors for news events using a network. We use recently proposed network embedding techniques, which are proven to be very effective for various prediction tasks in networks. As events involve different classes of nodes, such as named entities, temporal information, etc, general purpose network embeddings are agnostic to event semantics. To address this problem, we propose biased random walks that are tailored to capture the neighborhoods of news events in event networks. We then show that these learned embeddings are effective for news event recommendation and news event linking tasks using strong baselines, such as vanilla Node2Vec, and other state-of-the-art graph-based event ranking techniques.},

note = {Pre SFI},

keywords = {WP3: Media Content Production and Analysis},

pubstate = {published},

tppubtype = {conference}

}

@conference{Salatino2018,

title = {AUGUR: forecasting the emergence of new research topics},

author = {Angelo Antonio Salatino and Francesco Osborne and Enrico Motta},

url = {https://www.researchgate.net/publication/325492541_AUGUR_Forecasting_the_Emergence_of_New_Research_Topics},

year  = {2018},

date = {2018-05-01},

booktitle = {Proceedings of the 18th ACM/IEEE on Joint Conference on Digital Libraries},

pages = {303-312},

abstract = {Being able to rapidly recognise new research trends is strategic for many stakeholders, including universities, institutional funding bodies, academic publishers and companies. The literature presents several approaches to identifying the emergence of new research topics, which rely on the assumption that the topic is already exhibiting a certain degree of popularity and consistently referred to by a community of researchers. However, detecting the emergence of a new research area at an embryonic stage, i.e., before the topic has been consistently labelled by a community of researchers and associated with a number of publications, is still an open challenge. We address this issue by introducing Augur, a novel approach to the early detection of research topics. Augur analyses the diachronic relationships between research areas and is able to detect clusters of topics that exhibit dynamics correlated with the emergence of new research topics. Here we also present the Advanced Clique Percolation Method (ACPM), a new community detection algorithm developed specifically for supporting this task. Augur was evaluated on a gold standard of 1,408 debutant topics in the 2000-2011 interval and outperformed four alternative approaches in terms of both precision and recall.},

note = {Pre SFI},

keywords = {WP3: Media Content Production and Analysis},

pubstate = {published},

tppubtype = {conference}

}

@conference{Zhang2018,

title = {Ad Hoc Table Retrieval using Semantic Similarity},

author = {Shuo Zhang and Krisztian Balog},

url = {https://arxiv.org/pdf/1802.06159.pdf},

doi = {10.1145/3178876.3186067},

year  = {2018},

date = {2018-02-16},

booktitle = {Proceedings of The Web Conference 2018 (WWW’18)},

pages = {1553-1562},

abstract = {We introduce and address the problem of ad hoc table retrieval: answering a keyword query with a ranked list of tables. This task is not only interesting on its own account, but is also being used as a core component in many other table-based information access scenarios, such as table completion or table mining. The main novel contribution of this work is a method for performing semantic matching between queries and tables. Specifically, we (i) represent queries and tables in multiple semantic spaces (both discrete sparse and continuous dense vector representations) and (ii) introduce various similarity measures for matching those semantic representations. We consider all possible combinations of semantic representations and similarity measures and use these as features in a supervised learning model. Using a purpose-built test collection based on Wikipedia tables, we demonstrate significant and substantial improvements over a state-of-the-art baseline.},

note = {Pre SFI},

keywords = {Semantic matching, Semantic representations, Semantic similarity, Table retrieval, Table search, WP2: User Modeling Personalization and Engagement},

pubstate = {published},

tppubtype = {conference}

}

@conference{Arntzen2018b,

title = {Multi-device Linear Composition on the Web, Enabling Multi-device Linear Media with HTMLTimingObject and Shared Motion},

author = {Ingar Mæhlum Arntzen and Njål Borch and François Daoust and Dominique Hazael-Massieux

},

url = {https://www.researchgate.net/publication/324991987_Multi-device_Linear_Composition_on_the_Web_Enabling_Multi-device_Linear_Media_with_HTMLTimingObject_and_Shared_Motion},

year  = {2018},

date = {2018-01-01},

address = {Brussels},

organization = {Media Synchronization Workshop},

abstract = {Composition is a hallmark of the Web, yet it does not fully extend to linear media. This paper defines linear composition as the ability to form linear media by coordinated playback of independent linear components. We argue that native Web support for linear composition is a key enabler for Web-based multi-device linear media, and that precise multi-device timing is the main technical challenge. This paper proposes the introduction of an HTMLTimingObject as basis for linear composition in the single-device scenario. Linear composition in the multi-device scenario is ensured as HTMLTimingObjects may integrate with Shared Motion, a generic timing mechanism for the Web. By connecting HTMLMediaElements and HTMLTrackElements with a multi-device timing mechanism, a powerful programming model for multi-device linear media is unlocked.},

note = {Pre SFI},

keywords = {Linear Media, Multi-device, Shared Motion, WP4: Media Content Interaction and Accessibility},

pubstate = {published},

tppubtype = {conference}

}

@conference{Starke2017,

title = {Effective User Interface Designs to Increase Energy-efficient Behavior in a Rasch-based Energy Recommender System},

author = {Alain D. Starke and Martijn C. Willemsen and Chris C.P. Snijders},

url = {https://dl.acm.org/doi/abs/10.1145/3109859.3109902},

doi = {10.1145/3109859.3109902},

year  = {2017},

date = {2017-08-27},

number = {August 2017},

pages = {1-9},

abstract = {People often struggle to find appropriate energy-saving measures to take in the household. Although recommender studies show that tailoring a system's interaction method to the domain knowledge of the user can increase energy savings, they did not actually tailor the conservation advice itself. We present two large user studies in which we support users to make an energy-efficient behavioral change by presenting tailored energy-saving advice. Both systems use a one-dimensional, ordinal Rasch scale, which orders 79 energy-saving measures on their behavioral difficulty and link this to a user's energy-saving ability for tailored advice. We established that recommending Rasch-based advice can reduce a user's effort, increase system support and, in turn, increase choice satisfaction and lead to the adoption of more energy-saving measures. Moreover, follow-up surveys administered four weeks later point out that tailoring advice on its feasibility can support behavioral change.},

keywords = {Applied computing, Human-centered computing, Information Systems},

pubstate = {published},

tppubtype = {conference}

}

@conference{Elahi2017,

title = {Exploring the semantic gap for movie recommendations},

author = {Mehdi Elahi and Yashar Deldjoo and Farshad Bakhshandegan Moghaddam and Leonardo Cella and Stefano Cerada and Paolo Cremonesi },

url = {https://dl.acm.org/doi/pdf/10.1145/3109859.3109908},

doi = {https://doi.org/10.1145/3109859.3109908},

year  = {2017},

date = {2017-08-01},

booktitle = {Proceedings of the Eleventh ACM Conference on Recommender Systems},

pages = {326–330},

address = {New York},

organization = {Association for Computing Machinery},

abstract = {In the last years, there has been much attention given to the semantic gap problem in multimedia retrieval systems. Much effort has been devoted to bridge this gap by building tools for the extraction of high-level, semantics-based features from multimedia content, as low-level features are not considered useful because they deal primarily with representing the perceived content rather than the semantics of it.



In this paper, we explore a different point of view by leveraging the gap between low-level and high-level features. We experiment with a recent approach for movie recommendation that extract low-level Mise-en-Scéne features from multimedia content and combine it with high-level features provided by the wisdom of the crowd.



To this end, we first performed an offline performance assessment by implementing a pure content-based recommender system with three different versions of the same algorithm, respectively based on (i) conventional movie attributes, (ii) mise-en-scene features, and (iii) a hybrid method that interleaves recommendations based on movie attributes and mise-en-scene features. In a second study, we designed an empirical study involving 100 subjects and collected data regarding the quality perceived by the users. Results from both studies show that the introduction of mise-en-scéne features in conjunction with traditional movie attributes improves both offline and online quality of recommendations.},

note = {Pre SFI},

keywords = {Information Systems, Recommender systems, WP2: User Modeling Personalization and Engagement},

pubstate = {published},

tppubtype = {conference}

}

@conference{Elsweiler2017,

title = {Exploiting Food Choice Biases for Healthier Recipe Recommendation},

author = {David Elsweiler and Christoph Trattner and Morgan Harvey},

url = {https://www.christophtrattner.info/pubs/SIGIR2017.pdf},

year  = {2017},

date = {2017-08-01},

organization = {ACM SIGIR Conference},

abstract = {By incorporating healthiness into the food recommendation / ranking

process we have the potential to improve the eating habits of a

growing number of people who use the Internet as a source of food

inspiration. In this paper, using insights gained from various data

sources, we explore the feasibility of substituting meals that would

typically be recommended to users with similar, healthier dishes.

First, by analysing a recipe collection sourced from Allrecipes.com,

we quantify the potential for nding replacement recipes, which are

comparable but have dierent nutritional characteristics and are

nevertheless highly rated by users. Building on this, we present two

controlled user studies (n=107, n=111) investigating how people

perceive and select recipes. We show participants are unable to

reliably identify which recipe contains most fat due to their answers

being biased by lack of information, misleading cues and limited

nutritional knowledge on their part. By applying machine learning

techniques to predict the preferred recipes, good performance can

be achieved using low-level image features and recipe meta-data as

predictors. Despite not being able to consciously determine which

of two recipes contains most fat, on average, participants select

the recipe with the most fat as their preference. The importance of

image features reveals that recipe choices are often visually driven.

A nal user study (n=138) investigates to what extent the predictive

models can be used to select recipe replacements such that users

can be “nudged” towards choosing healthier recipes. Our ndings

have important implications for online food systems.},

note = {Pre SFI},

keywords = {Behavioural change, Food RecSys, Human decision making, Information behaviour},

pubstate = {published},

tppubtype = {conference}

}

@conference{Elahi2017b,

title = {Visually-Aware Video Recommendation in the Cold Start},

author = {Mehdi Elahi and Reza Hosseini and Mohammad Hossein Rimaz and Farshad B. Moghaddam and Christoph Trattner},

url = {https://christophtrattner.com/pubs/ht2020.pdf},

year  = {2017},

date = {2017-07-01},

urldate = {2017-07-01},

pages = {1-5},

organization = {Proccedings of theACM Hypertext 2020},

abstract = {Recommender Systems (RSs) have become essential tools in any

video-sharing platforms (such as YouTube) by generating video

suggestions for users. Although, RSs have been e!ective, however,

they su!er from the so-called New Item problem. New item problem,

as part of Cold Start problem, happens when a new item is added to

the system catalogue and the RS has no or little data available for

that new item. In such a case, the system may fail to meaningfully

recommend the new item to any user.

In this paper, we propose a novel recommendation technique

based on visual tags, i.e., tags that are automatically annotated

to videos based on visual description of videos. Such visual tags

can be used in an extreme cold start situation, where neither any

rating, nor any tag is available for the new video item. The visual

tags could also be used in the moderate cold start situation when

the new video item has been annotated with few tags. This type

of content features can be extracted automatically without any

human involvement and have been shown to be very e!ective in

representing the video content.

We have used a large dataset of videos and shown that automatically 

extracted visual tags can be incorporated into the cold start

recommendation process and achieve superior results compared to

the recommendation based on human-annotated tags.},

note = {Pre SFI},

keywords = {Video Recommendation},

pubstate = {published},

tppubtype = {conference}

}

@conference{Borch2016,

title = {Timing - small step for developers, giant leap for the media industry, IBC 2016},

author = {Njål Borch and François Daoust and Ingar Mæhlum Arntzen},

url = {https://www.w3.org/community/webtiming/files/2016/09/Borch_IBC2016-final.pdf},

year  = {2017},

date = {2017-02-11},

note = {Pre SFI},

keywords = {WP4: Media Content Interaction and Accessibility},

pubstate = {published},

tppubtype = {conference}

}

@conference{Setty2017,

title = {Modeling event importance for ranking daily news events},

author = {Vinay Setty and Abhijit Anand and Arunav Mishra and Avishek Anand},

url = {https://dl.acm.org/doi/pdf/10.1145/3018661.3018728},

doi = {10.1145/3018661.3018728},

year  = {2017},

date = {2017-02-01},

booktitle = {Proceedings of the tenth acm international conference on web search and data mining},

pages = {231–240},

address = {New York},

organization = {Association for Computing Machinery},

abstract = {We deal with the problem of ranking news events on a daily basis for large news corpora, an essential building block for news aggregation. News ranking has been addressed in the literature before but with individual news articles as the unit of ranking. However, estimating event importance accurately requires models to quantify current day event importance as well as its significance in the historical context. Consequently, in this paper we show that a cluster of news articles representing an event is a better unit of ranking as it provides an improved estimation of popularity, source diversity and authority cues. In addition, events facilitate quantifying their historical significance by linking them with long-running topics and recent chain of events. Our main contribution in this paper is to provide effective models for improved news event ranking.



To this end, we propose novel event mining and feature generation approaches for improving estimates of event importance. Finally, we conduct extensive evaluation of our approaches on two large real-world news corpora each of which span for more than a year with a large volume of up to tens of thousands of daily news articles. Our evaluations are large-scale and based on a clean human curated ground-truth from Wikipedia Current Events Portal. Experimental comparison with a state-of-the-art news ranking technique based on language models demonstrates the effectiveness of our approach.},

note = {Pre SFI},

keywords = {WP3: Media Content Production and Analysis},

pubstate = {published},

tppubtype = {conference}

}