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Dirichlet-Luce choice model for learning from interactions
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AI
DOI：10.1007/s11257-022-09331-0.png)
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Abstract
En 中文
We propose a Bayesian choice model, the Dirichlet-Luce model, for recommender systems that interact with users in a feedback loop. The model is built on a generalization of the Dirichlet distribution and the assumption that the users of a recommender system choose from a subset of all items that are systematically presented based on their previous choices. The model allows efficient inference of user preferences. Its Bayesian construction leads to a bandit algorithm-based on Thompson sampling-for online learning to recommend, which achieves low regret measured in terms of the inherent attractiveness of the options included in the recommendations. The combined setup also eliminates some biases recommender systems might be prone to, where popular, promoted, or initially preferred items are overestimated due to overexposure, or underrepresented items in the recommendations are underestimated. Our model has a potential to be reused as a fundamental building block for recommender systems.
Keywords:
Choice modeling
Implicit feedback recommendation
Bandit algorithms
Journal
U
IF:
3.5
Papers:
532
Citations:
1.7K