A Bayesian Choice Model for Eliminating Feedback Loops
This addresses the problem of feedback loops in interactive systems like recommender systems, offering a practical solution with incremental improvements.
The paper tackles self-reinforcing feedback loops in personalization systems by proposing a Bayesian choice model that accounts for users' limited exposure to alternatives, achieving low regret with exploration of only a small fraction of possible presentations.
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.