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

Publications

2024

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Alan Said, Eva Zangerle and Christine Bauer: Report on the 3rd Workshop on the Perspectives on the Evaluation of Recommender Systems (PERSPECTIVES 2023) at RecSys 2023. In SIGIR Forum, vol. 57, no. 2. Association for Computing Machinery, 2024

2023

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Christine Bauer, Eva Zangerle and Alan Said: Exploring the Landscape of Recommender Systems Evaluation: Practices and Perspectives. In ACM Transactions on Recommender Systems. Association for Computing Machinery, 2023 Just Accepted

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Amir Reza Mohammadi: Explainable Graph Neural Network Recommenders; Challenges and Opportunities. In Proceedings of the 17th ACM Conference on Recommender Systems, RecSys 2023, Singapore, Singapore, September 18-22, 2023, pages 1318-1324. ACM, 2023

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Andreas Peintner: Sequential Recommendation Models: A Graph-based Perspective. In Proceedings of the 17th ACM Conference on Recommender Systems, RecSys 2023, Singapore, Singapore, September 18-22, 2023, pages 1295-1299. ACM, 2023

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Andreas Peintner, Amir Reza Mohammadi and Eva Zangerle: SPARE: Shortest Path Global Item Relations for Efficient Session-based Recommendation. In Proceedings of the 17th ACM Conference on Recommender Systems, RecSys 2023, Singapore, Singapore, September 18-22, 2023, pages 58-69. ACM, 2023

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Alan Said, Eva Zangerle, and Christine Bauer: Proceedings of the 3rd Workshop Perspectives on the Evaluation of Recommender Systems 2023 co-located with the 17th ACM Conference on Recommender Systems (RecSys 2023), Singapore, Singapore, September 19, 2023. Vol. 3476. CEUR-WS.org, 2023

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Alan Said, Eva Zangerle and Christine Bauer: Third Workshop: Perspectives on the Evaluation of Recommender Systems (PERSPECTIVES 2023). In Proceedings of the 17th ACM Conference on Recommender Systems, RecSys 2023, Singapore, Singapore, September 18-22, 2023, pages 1221-1222. ACM, 2023

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Eva Zangerle and Christine Bauer: Evaluating Recommender Systems: Survey and Framework. In ACM Computing Surveys, vol. 55, no. 8. Association for Computing Machinery, 2023

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Eva Zangerle, Christine Bauer and Alan Said: Report on the 2nd Workshop on the Perspectives on the Evaluation of Recommender Systems (PERSPECTIVES 2022) at RecSys 2022. In SIGIR Forum, vol. 56, no. 2. Association for Computing Machinery, 2023.

2022

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Eva Zangerle: Recommender Systems for Music Retrieval Tasks. Habilitation Thesis, University of Innsbruck, 2022

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Maximilian Mayerl, Stefan Brandl, Günther Specht, Markus Schedl and Eva Zangerle: Verse versus Chorus: Structure-aware Feature Extraction for Lyrics-based Genre Recognition. In Proceedings of the 23rd International Society for Music Information Retrieval Conference 2022, pages 884-890. ISMIR, 2022

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Marta Moscati, Emilia Parada-Cabaleiro, Yashar Deldjoo, Eva Zangerle and Markus Schedl: Music4All-Onion - A Large-Scale Multi-Faceted Content-Centric Music Recommendation Dataset. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management, pages 4339–4343. Association for Computing Machinery, 2022

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Eva Zangerle, Christine Bauer and Alan Said: Proceedings of the Perspectives on the Evaluation of Recommender Systems Workshop 2022, co-located with the 16th ACM Conference on Recommender Systems (RecSys 2022). Vol. 3228. CEUR-WS.org, 2022

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Eva Zangerle, Christine Bauer and Alan Said: Second Workshop: Perspectives on the Evaluation of Recommender Systems (PERSPECTIVES 2022). In Proceedings of the 16th ACM Conference on Recommender Systems, pages 652–653. Association for Computing Machinery, 2022

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Eva Zangerle, Christine Bauer and Alan Said: Report on the 1st Workshop on the Perspectives on the Evaluation of Recommender Systems (PERSPECTIVES 2021) at RecSys 2021. In SIGIR Forum, vol. 55, no. 2. Association for Computing Machinery, 2022

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