Presenting Challenging Recommendations: Making Diverse News Acceptable

Recommender systems find relevant content for us online, including the personalized news we increasingly receive on Twitter and Facebook. As a consequence of personalization, we increasingly see content that agrees with our views, we cease to be exposed to views contrary to our own. Both algorithms and the users themselves filter content, and this creates more polarized points of view, so called “filter bubbles” or “echo chambers”. This paper presents a vision of a diversity aware recommendation model, for the selection and presentation of a diverse selection of news to users. This diversity aware recommendation model considers that: a) users have different requirements on diversity (e.g., challenge-averse or diversity seeking), and that b) items will satisfy these requirements to different extents (e.g., liberal or conservative news). By considering both item and user diversity this model aims to maximize the amount of diverse content that users are exposed to, without damaging system reputation.Web Information System

MovieTweeters: An Interactive Interface to Improve Recommendation Novelty

This paper introduces and evaluates a novel interface, MovieTweeters. It is a movie recommendation system which incorporates social information with a traditional recommendation algorithm to generate recommendations for users. Few previous studies have investigated the influence of using social information in interactiveinterfaces to improve the novelty of recommendations. To address this gap, we investigate whether social information can be incorporated effectively into an interactive interface to improve recommendation novelty and user satisfaction. Our initial results suggest that such an interactive interface does indeed help users discovermore novel items. Also, we observed users who perceived that they discovered more novel and diverse items reported increased levelsof user satisfaction. Surprisingly, we observed that even though we successfully were able to increase the system diversity of the recommendations, it had a negative correlation with users perception of novelty and diversity of the items highlighting the importance of improved user-centered approaches.Accepted author manuscriptWeb Information System

Generating Consensus Explanations for Group Recommendations: An exploratory study

In some scenarios, like music, people often consume items in groups. However, reaching a consensus is difficult, and often compromises need to be made. Such compromises can potentially help users expand their tastes. They can also lead to outright rejection of the recommended items. One way to avoid this is to explain recommendations that are surprising, or even expected to be disliked, by an individual user. This paper presents an approach for generating explanations for groups. We propose algorithms for selecting a sequence of songs for a group to consume. These algorithms consider consensus but have different trade-offs. Next, using these algorithms we generated explanations in a layered evaluation using synthetic data. We studied the influence of these explanations in structured interviews with users (n=16) on user satisfactionUMAP Late breaking Results Accepted author manuscriptWeb Information System

A Diversity Adjusting Strategy with Personality for Music Recommendation

Diversity-based recommender systems aim to select a wide rangeof relevant content for users, but diversity needs for users withdifferent personalities are rarely studied. Similarly, research onpersonality-based recommender systems has primarily focused onthe ‘cold-start problem’; few previous works have investigated howpersonality influences users’ diversity needs. This paper combinesthese two branches of research together: re-ranking for diversifica-tion, and improving accuracy using personality traits. Anchoredin the music domain, we investigate how personality informationcan be used to adjust the diversity degrees for people with differentpersonalities. We proposed a personality-based diversification algo-rithm to help enhance the diversity adjusting strategy according topeople’s personality information in music recommendations. Ouroffline and online evaluation results demonstrate that our proposedmethod is an effective solution to generate personalized recommen-dation lists that not only have relatively higher diversity as well asaccuracy, but which also lead to increased user satisfaction.Web Information System

Towards Analogy-based Recommendation: Benchmarking of Perceived Analogy Semantics

Requests for recommendation can be seen as a form of query for candidate items, ranked by relevance. Users are however o‰enunable to crisply de€ne what they are looking for. One of the core concepts of natural communication for describing and explainingcomplex information needs in an intuitive fashion are analogies: e.g., “What is to Christopher Nolan as is 2001: A Space Odyssey toStanley Kubrick?”. Analogies allow users to explore the item space by formulating queries in terms of items rather than explicitlyspecifying the properties that they €nd aŠractive. One of the core challenges which hamper research on analogy-enabled queries isthat analogy semantics rely on consensus on human perception, which is not well represented in current benchmark data sets. Œerefore, in this paper we introduce a new benchmark dataset focusing on the human aspects for analogy semantics. Furthermore, we evaluate a popular technique for analogy semantics (word2vec neuronal embeddings) using our dataset. Œe results show that current word embedding approaches are still not not suitable to su�ciently deal with deeper analogy semantics. We discuss future directions including hybrid algorithms also incorporating structural or crowd-based approaches, and the potential for analogy-based explanations.Web Information System

Helping users discover perspectives: Enhancing opinion mining with joint topic models

Support or opposition concerning a debated claim such as abortion should be legal can have different underlying reasons, which we call perspectives. This paper explores how opinion mining can be enhanced with joint topic modeling, to identify distinct perspectives within the topic, providing an informative overview from unstructured text. We evaluate four joint topic models (TAM, JST, VODUM, and LAM) in a user study assessing human understandability of the extracted perspectives. Based on the results, we conclude that joint topic models such as TAM can discover perspectives that align with human judgments. Moreover, our results suggest that users are not influenced by their pre-existing stance on the topic of abortion when interpreting the output of topic models.Virtual/online event due to COVID-19Web Information System

Effects of Personal Characteristics on Music Recommender Systems with Different Levels of Controllability

Previous research has found that enabling users to control the recommendation process increases user satisfaction. However, providing additional controls also increases cognitive load, and different users have different needs for control. Therefore, in this study, we investigate the effect of two personal characteristics: musical sophistication and visual memory capacity. We designed a visual user interface, on top of a commercial music recommender, with different controls: interactions with recommendations (i.e., the output of a recommender system), the user profile (i.e., the top listened songs), and algorithm parameters (i.e., weights in an algorithm). We created eight experimental settings with combinations of these three user controls and conducted a between-subjects study (N=240), to explore the effect on cognitive load and recommendation acceptance for different personal characteristics. We found that controlling recommendations is the most favorable single control element. In addition, controlling user profile and algorithm parameters was the most beneficial setting with multiple controls. Moreover, the participants with high musical sophistication perceived recommendations to be of higher quality, which in turn lead to higher recommendation acceptance. However, we found no effect of visual working memory on either cognitive load or recommendation acceptance. This work contributes an understanding of how to design control that hits the sweet spot between the perceived quality of recommendations and acceptable cognitive load.Accepted author manuscriptWeb Information System

Someone really wanted that song but it was not me!: Evaluating Which Information to Disclose in Explanations for Group Recommendations

Explanations can be used to supply transparency in recommender systems (RSs). However, when presenting a shared explanation to a group, we need to balance users' need for privacy with their need for transparency. This is particularly challenging when group members have highly diverging tastes and individuals are confronted with items they do not like, for the benefit of the group. This paper investigates which information people would like to disclose in explanations for group recommendations in the music domain.Web Information System

Toward Natural Language Mitigation Strategies for Cognitive Biases in Recommender Systems

Cognitive biases in the context of consuming online information filtered by recommender systems may lead to sub-optimal choices. One approach to mitigate such biases is through interface and interaction design. This survey reviews studies focused on cognitive bias mitigation of recommender system users during two processes: 1) item selection and 2) preference elicitation. It highlights a number of promising directions for Natural Language Generation research for mitigating cognitive bias including: the need for personalization, as well as for transparency and control.Web Information System

Memorability of Semantically Grouped Online Reviews

This paper evaluates whether semantic grouping of reviews helps users make better decisions. Reviews rated as helpful were compared with semantically grouped reviews. While participants did not perceive a reduced effort (using NASA-TLX), they needed less time and performed better on answering questions about the strong,weak and controversial points of the movies.Electrical Engineering, Mathematics and Computer ScienceWeb Information System