Paper Session 2: User Side of Recommender Systems

Date: Monday, Sept 16, 2019, 16:00-17:30
Location: Auditorium
Chair: Michael Ekstrand

  • LPUsers in the Loop: A Psychologically-Informed Approach to Similar Item Retrieval
    by Amy A. Winecoff, Florin Brasoveanu, Bryce Casavant, Pearce Washabaugh, Matthew Graham

    Recommender systems (RS) often leverage information about the similarity between items’ features to make recommendations. Yet, many commonly used similarity functions make mathematical assumptions such as symmetry (i.e., Sim(a,b) = Sim(b,a)) that are inconsistent with how humans make similarity judgments. Moreover, most algorithm validations either do not directly measure users’ behavior or fail to comply with methodological standards for psychological research. RS that are developed and evaluated without regard to users’ psychology may fail to meet users’ needs. To provide recommendations that do meet the needs of users, we must: 1) develop similarity functions that account for known properties of human cognition, and 2) rigorously evaluate the performance of these functions using methodologically sound user testing. Here, we develop a framework for evaluating users’ judgments of similarity that is informed by best practices in psychological research methods. Employing users’ fashion item similarity judgments collected using our framework, we demonstrate that a psychologically-informed similarity function (i.e., Tversky contrast model) outperforms a psychologically-naive similarity function (i.e., Jaccard similarity) in predicting users’ similarity judgments.

  • LPDesigning for the Better by Taking Users into Account: A Qualitative Evaluation of User Control Mechanisms in (News) Recommender Systems
    by Jaron Harambam, Dimitrios Bountouridis, Mykola Makhortykh, Joris van Hoboken

    Recommender systems (RS) are on the rise in many domains. While they offer great promises, they also raise concerns: lack of transparency, reduction of diversity, little to no user control. In this paper, we align with the normative turn in computer science which scrutinizes the ethical and societal implications of RS. We focus and elaborate on the concept of user control because that mitigates multiple problems at once. Taking the news industry as our domain, we conducted four focus groups, or moderated think-aloud sessions, with Dutch news readers (N=21) to systematically study how people evaluate different control mechanisms (at the input, process, and output phase) in a News Recommender Prototype (NRP). While these mechanisms are sometimes met with distrust about the actual control they offer, we found that an intelligible user profile (including reading history and flexible preferences settings), coupled with possibilities to influence the recommendation algorithms is highly valued, especially when these control mechanisms can be operated in relation to achieving personal goals. By bringing (future) users’ perspectives to the fore, this paper contributes to a richer understanding of why and how to design for user control in recommender systems.

  • LPEfficient Privacy-Preserving Recommendations based on Social Graphs
    by Aidmar Wainakh, Tim Grube, Jörg Daubert, Max Mühlhäuser

    Many recommender systems use association rules mining, a technique that captures relations between user interests and recommends new probable ones accordingly. Applying association rule mining causes privacy concerns as user interests may contain sensitive personal information (e.g., political views). This potentially even inhibits the user from providing information in the first place. Current distributed privacy-preserving association rules mining (PPARM) approaches use cryptographic primitives that come with high computational and communication costs, rendering PPARM unsuitable for large-scale applications such as social networks. We propose improvements on the efficiency and the privacy of PPARM approaches by minimizing the required data. We propose and compare sampling strategies to sample the data based on social graphs in a privacy-preserving manner. The results on real-world datasets show that our sampling-based approach can achieve a high average precision score with as low as 50% sampling rate and, therefore, with a 50% reduction of communication cost.

  • LPPrivateJobMatch: A Privacy-Oriented Deferred Multi-Match Recommender System for Stable Employment
    by Amar Saini, Florin Rusu, Andrew Johnston

    Coordination failure reduces match quality among employers and candidates in the job market, resulting in a large number of unfilled positions and/or unstable, short-term employment. Centralized job search engines provide a platform that connects directly employers with job-seekers. However, they require users to disclose a significant amount of personal data, i.e., build a user profile, in order to provide meaningful recommendations. In this paper, we present PrivateJobMatch — a privacy-oriented deferred multi-match recommender system — which generates stable pairings while requiring users to provide only a partial ranking of their preferences. PrivateJobMatch explores a series of adaptations of the game-theoretic Gale-Shapley deferred acceptance algorithm which combine the flexibility of decentralized markets with the intelligence of centralized matching. We identify the shortcomings of the original algorithm when applied to a job market and propose novel solutions that rely on machine learning techniques. Experimental results on real and synthetic data confirm the benefits of the proposed algorithms across several quality measures. Over the past year, we have implemented a PrivateJobMatch prototype and deployed it in an active job market economy. Using the gathered real-user preference data, we find that the match recommendations are superior to a typical decentralized job market—while requiring only a partial ranking of the user preferences.

  • SPOUser-Centered Evaluation of Strategies for Recommending Sequences of Points of Interest to Groups
    by Daniel Herzog, Wolfgang Wörndl

    Most recommender systems (RSs) predict the preferences of individual users; however, in certain scenarios, recommendations need to be made for a group of users. Tourism is a popular domain for group recommendations because people often travel in groups and look for point of interest (POI) sequences for their visits during a trip. In this study, we present different strategies that can be used to recommend POI sequences for groups. In addition, we introduce novel approaches, including a strategy called Split Group, which allows groups to split into smaller groups during a trip. We compared all strategies in a user study with 40 real groups. Our results proved that there was a significant difference in the quality of recommendations generated by using the different strategies. Most groups were willing to split temporarily during a trip, even when they were traveling with persons close to them. In this case, Split Group generated the best recommendations for different evaluation criteria. We use these findings to propose improvements for group recommendation strategies in the tourism domain.

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