Bayesian-Guided Diversity in Sequential Sampling for Recommender Systems

• URL: http://arxiv.org/abs/2506.21617v1
• Date: Sun, 22 Jun 2025 19:36:02 GMT
• Title: Bayesian-Guided Diversity in Sequential Sampling for Recommender Systems
• Authors: Hiba Bederina, Jill-Jênn Vie,
• Abstract summary: We propose a novel framework that leverages a multi-objective, contextual sequential sampling strategy.<n>Item selection is guided by Bayesian updates that dynamically adjust scores to optimize diversity.<n> Experiments on a real-world dataset show that our approach significantly improves diversity without sacrificing relevance.
• Score: 1.675857332621569
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The challenge of balancing user relevance and content diversity in recommender systems is increasingly critical amid growing concerns about content homogeneity and reduced user engagement. In this work, we propose a novel framework that leverages a multi-objective, contextual sequential sampling strategy. Item selection is guided by Bayesian updates that dynamically adjust scores to optimize diversity. The reward formulation integrates multiple diversity metrics-including the log-determinant volume of a tuned similarity submatrix and ridge leverage scores-along with a diversity gain uncertainty term to address the exploration-exploitation trade-off. Both intra- and inter-batch diversity are modeled to promote serendipity and minimize redundancy. A dominance-based ranking procedure identifies Pareto-optimal item sets, enabling adaptive and balanced selections at each iteration. Experiments on a real-world dataset show that our approach significantly improves diversity without sacrificing relevance, demonstrating its potential to enhance user experience in large-scale recommendation settings.

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