Session: Women in RecSys: Journal Paper of the Year Awards

Date: Wednesday September 21, 4:00 PM – 5:00 PM (PDT)

  • JuniorA compositional model of multi-faceted trust for personalized item recommendation
    by Liliana Ardissono (University of Torino), Noemi Mauro (University of Torino)

    Trust-based recommender systems improve rating prediction with respect to Collaborative Filtering by leveraging the additional information provided by a trust network among users to deal with the cold start problem. However, they are challenged by recent studies according to which people generally perceive the usage of data about social relations as a violation of their own privacy. In order to address this issue, we extend trust-based recommender systems with additional evidence about trust, based on public anonymous information, and we make them configurable with respect to the data that can be used in the given application domain: 1. We propose the Multi-faceted Trust Model (MTM) to define trust among users in a compositional way, possibly including or excluding the types of information it contains. MTM flexibly integrates social links with public anonymous feedback received by user profiles and user contributions in social networks. 2. We propose LOCABAL+, based on MTM, which extends the LOCABAL trust-based recommender system with multi-faceted trust and trust-based social regularization. Experiments carried out on two public datasets of item reviews show that, with a minor loss of user coverage, LOCABAL+ outperforms state-of-the art trust-based recommender systems and Collaborative Filtering in accuracy, ranking of items and error minimization both when it uses complete information about trust and when it ignores social relations. The combination of MTM with LOCABAL+ thus represents a promising alternative to state-of-the-art trust-based recommender systems.

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  • SeniorDiversity by design in music recommender systems
    by Lorenzo Porcaro (Universitat Pompeu Fabra), Carlos Castillo (Universitat Pompeu Fabra), Emilia Gómez (Universitat Pompeu Fabra)

    Music Recommender Systems (Music RS) are nowadays pivotal in shaping the listening experience of people all around the world. Partly driven by the commercial application of this technology, music recommendation research has gained increasing attention both within and outside the Music Information Retrieval (MIR) community. Thanks also to the widespread use of recommender systems in music streaming services, it has been possible to enhance several characteristics of such systems in terms of performance, design, and user experience. Nonetheless, imagining Music RS only from an application-driven perspective may generate an incomplete view of how this technology is affecting people’s habitus, from the decision-making processes to the formation of musical taste and opinions. In this overview, we address the concept of diversity in music recommendation, and taking a value-driven approach we review diversity-related methodologies proposed in the Music RS literature. Additionally, by taking as an example the wider context of Information Technology (IT), we present the elements interacting in the diversity by design paradigm. We do that to acknowledge the lack of a comprehensive framework in Music RS research to address diversity, until now mostly driven by empirical results and fragmented in different application areas. Maintaining an interdisciplinary perspective, we discuss some challenges that MIR practitioners may face when researching Music RS, going beyond the search for better performance and instead questioning the theoretical foundations on which to base future research.

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  • SeniorLeveraging affective hashtags for ranking music recommendations
    by Eva Zangerle (University of Innsbruck), Chih-Ming Chen (National Chengchi University), Ming-Feng Tsai (National Chengchi University), Yi-Hsuan Yang (Academia Sinica)

    Mood and emotion play an important role when it comes to choosing musical tracks to listen to. In the field of music information retrieval and recommendation, emotion is considered contextual information that is hard to capture, albeit highly influential. In this study, we analyze the connection between users` emotional states and their musical choices. Particularly, we perform a large-scale study based on two data sets containing 560,000 and 90,000 #nowplaying tweets, respectively. We extract affective contextual information from hashtags contained in these tweets by applying an unsupervised sentiment dictionary approach. Subsequently, we utilize a state-of-the-art network embedding method to learn latent feature representations of users, tracks and hashtags. Based on both the affective information and the latent features, a set of eight ranking methods is proposed. We find that relying on a ranking approach that incorporates the latent representations of users and tracks allows for capturing a user’s general musical preferences well (regardless of used hashtags or affective information). However, for capturing context-specific preferences (a more complex and personal ranking task), we find that ranking strategies that rely on affective information and that leverage hashtags as context information outperform the other ranking strategies.

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