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Beyond the Rating Matrix: Debiasing Implicit Feedback Loops in Collaborative Filtering

Thorsten Krause; Daniel Stattkus; Alina Deriyeva; Jan Heinrich Beinke; Oliver Thomas
In: Wirtschaftsinformatik 2022 Proceedings. Internationale Tagung Wirtschaftsinformatik (WI-2022), February 21-23, Nürnberg, Germany, AISeL, 2022.


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 systemsthat 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.