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arXiv · 1908.05640

A Bayesian Choice Model for Eliminating Feedback Loops

Gökhan Çapan·Ilker Gündoğdu·Ali Caner Türkmen·Çağrı Sofuoğlu·Ali Taylan Cemgil
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Abstract

Self-reinforcing feedback loops in personalization systems are typically caused by users choosing from a limited set of alternatives presented systematically based on previous choices. We propose a Bayesian choice model built on Luce axioms that explicitly accounts for users' limited exposure to alternatives. Our model is fair---it does not impose negative bias towards unpresented alternatives, and practical---preference estimates are accurately inferred upon observing a small number of interactions. It also allows efficient sampling, leading to a straightforward online presentation mechanism based on Thompson sampling. Our approach achieves low regret in learning to present upon exploration of only a small fraction of possible presentations. The proposed structure can be reused as a building block in interactive systems, e.g., recommender systems, free of feedback loops.

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Gökhan Çapan, Ilker Gündoğdu, Ali Caner Türkmen, Çağrı Sofuoğlu, Ali Taylan Cemgil. 2019-08-15. A Bayesian Choice Model for Eliminating Feedback Loops. https://arxiv.org/abs/1908.05640

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