Recommender Systems and User Modeling

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Recommender systems are ubiquitous in the digital world and largely determine the options that humans get to choose from on web platforms, from online shopping to music streaming. Recommender systems are mostly built upon statistics of past collective user behavior to mimic human preference and decision-making, assuming that users like what similar users liked in the past (so-called collaborative filtering). 

Given their success, such systems indeed seem to capture some of the underlying factors. However, to date, RS do not fully capture the factors leading to human decision-making. We still lack an understanding of important factors such as user intent (i.e., why people listen to music or shop for a particular item) and context-specific decision making (i.e., a user behaving differently in different contexts). 

Our research investigates how such comprehensive user information can effectively be captured in a user model that can be leveraged by recommender systems. Such models need to account for multi-faceted, context-specific user preferences and intents while allowing efficient computation and aggregation. Furthermore, we also advance recommender systems that allow leveraging such comprehensive user models.


Photo by Roman Odintsov at Pexels.

Team

Current Theses

Currently running

Publications

2021

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Eva Zangerle , Christine Bauer and Alan Said: Proceedings of the Perspectives on the Evaluation of Recommender Systems Workshop 2021, co-located with the 15th ACM Conference on Recommender Systems (RecSys 2021). Vol. 2955. CEUR-WS.org, 2021

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Eva Zangerle, Christine Bauer and Alan Said: Perspectives on the Evaluation of Recommender Systems (PERSPECTIVES). In Fifteenth ACM Conference on Recommender Systems, pages 794–795. Association for Computing Machinery, 2021

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Martin Pichl and Eva Zangerle: User models for multi-context-aware music recommendation. In Multimedia Tools and Applications, vol. 80, no. 15, pages 22509-22531. Springer, 2021

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Eva Zangerle, Chih-Ming Chen, Ming-Feng Tsai and Yi-Hsuan Yang: Leveraging Affective Hashtags for Ranking Music Recommendations. In IEEE Transactions on Affective Computing, vol. 12, no. 1, pages 78-91. 2021

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Dominik Kowald, Peter Muellner, Eva Zangerle, Christine Bauer, Markus Schedl and Elisabeth Lex: Support the underground: characteristics of beyond-mainstream music listeners. In EPJ Data Science, vol. 10, no. 1, pages 1-26. Springer, 2021

2020

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Eva Zangerle, Martin Pichl and Markus Schedl: User Models for Culture-Aware Music Recommendation: Fusing Acoustic and Cultural Cues. In Transactions of the International Society for Music Information Retrieval, vol. 3, no. 1. Ubiquity Press, 2020

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Alessandro B. Melchiorre, Eva Zangerle and Markus Schedl: Personality Bias of Music Recommendation Algorithms. In 14th ACM Conference on Recommender Systems (RecSys 2020), pages 533–538. ACM, 2020.

2019

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Christine Bauer and Eva Zangerle: Leveraging Multi-Method Evaluation for Multi-Stakeholder Settings. In Proceedings of the 1st Workshop on the Impact of Recommender Systems co-located with 13th ACM Conference on Recommender Systems (ACM RecSys 2019). ceur-ws.org, 2019

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Maximilian Mayerl, Michael Vötter, Eva Zangerle and Günther Specht: Language Models for Next-Track Music Recommendation. In Proceedings of the 31st GI-Workshop Grundlagen von Datenbanken, Saarburg, Germany, June 11-14, 2019., pages 15-19. 2019

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Michael Vötter, Eva Zangerle, Maximilian Mayerl and Günther Specht: Autoencoders for Next-Track-Recommendation. In Proceedings of the 31st GI-Workshop Grundlagen von Datenbanken, Saarburg, Germany, June 11-14, 2019., pages 20-25. 2019

2018

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Martin Pichl and Eva Zangerle: Latent Feature Combination for Multi-Context Music Recommendation. In 2018 International Conference on Content-Based Multimedia Indexing (CBMI), pages 1-6. 2018

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Martin Pichl, Bernward Pichl and Eva Zangerle: Carl: Sports Award Recommender. In The SIGIR 2018 Workshop On eCommerce co-located with the 41st International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2018), Ann Arbor, Michigan, USA, July 12, 2018., vol. 2319. CEUR-WS.org, 2018

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Asmita Poddar, Eva Zangerle and Yi-Hsuan Yang : #nowplaying-RS: A New Benchmark Dataset for Building Context-Aware Music Recommender Systems. In Proceedings of the 15th Sound & Music Computing Conference. 2018

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Ilknur Celik, Ilaria Torre, Frosina Koceva, Christine Bauer, Eva Zangerle and Bart Knijnenburg: Intelligent User-Adapted Interfaces: Design and Multi-Modal Evaluation (IUadaptMe) Workshop Chairs' Welcome and Organization. In Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization (UMAP 2018), pages 137-139. ACM, 2018

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Eva Zangerle, Martin Pichl and Markus Schedl: Culture-Aware Music Recommendation. In Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization (UMAP 2018), pages 357-358. ACM, 2018

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Eva Zangerle and Claudia Müller-Birn: Recommendation-Assisted Data Curation for Wikidata. In Wiki Workshop 2018 co-located with The Web Conference. 2018

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Christian Esswein, Markus Schedl and Eva Zangerle: geMsearch: Personalized Explorative Music Search. In Joint Proceedings of the ACM IUI 2018 Workshops co-located with the 23rd ACM Conference on Intelligent User Interfaces (ACM IUI 2018). ceur-ws.org, 2018

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Christine Bauer and Eva Zangerle: Information Imbalance and Responsibility in Recommender Systems. In Workshop Proceeding of the 2nd Workshop on Green (Responsible, Ethical and Social) IT and IS ? the Corporate Perspective (GRES-IT/IS). 2018

2017

Bib

Adelheid Heftberger, Jakob Höper, Claudia Müller-Birn, Niels-Oliver Walkowski and Eva Zangerle: Employing Wikidata for Fostering Scholarly Research. WikiDataCon 2017, Berlin, available online at https://www.wikidata.org/wiki/Wikidata:WikidataCon_2017/Submissions/Employing_Wikidata_for_Fostering_Scholarly_Research

Bib Link

Martin Pichl, Eva Zangerle and Günther Specht: Improving Context-Aware Music Recommender Systems: Beyond the Pre-filtering Approach. In Proceedings of the 2017 ACM International Conference on Multimedia Retrieval (ICMR 2017), pages 201-208. ACM, 2017