Beyond the Rating Matrix: Debiasing Implicit Feedback Loops in Collaborative Filtering
Autor(en): | Krause, Thorsten Stattkus, Daniel Deriyeva, Alina Beinke, Jan Heinrich Thomas, Oliver |
Stichwörter: | Collaborative filtering; Collaborative filtering recommender systems; De-biasing; discrete choice; exposure bias; Feedback; feedback loop; Feedback loops; implicit feedback; matrix; Mitigation strategy; Performance; Practical method; recommender systems | Erscheinungsdatum: | 2022 | Herausgeber: | Association for Information Systems | Journal: | 17th International Conference on Wirtschaftsinformatik, WI 2022 | Zusammenfassung: | Implicit feedback collaborative filtering recommender systems suffer from exposure bias that corrupts performance and creates filter bubbles and echo chambers. Our study aims to provide a practical method that does not inherit any exposure bias from the data given the information about the user, the choice, and the choice set associated with each observation. We validated the model's functionality and capability to reduce bias and compared it to baseline mitigation strategies by simulation. Our model inherited little to no bias, while the other approaches failed to mitigate all bias. To the best of our knowledge, we are first to identify a feasible approach to tackle exposure bias in recommender systems that does not require arbitrary parameter choices or large model extensions. With our findings, we encourage the recommender systems community to move away from rating-matrix-based towards discrete-choice-based models. © 2022 17th International Conference on Wirtschaftsinformatik, WI 2022. All rights reserved. |
Beschreibung: | Cited by: 1; Conference name: 17th International Conference on Wirtschaftsinformatik, WI 2022; Conference date: 21 February 2022 through 23 February 2022; Conference code: 191912 |
Externe URL: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135918025&partnerID=40&md5=1e9760e1449d12db8a3249d3b0a5afa5 |
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