Bayesian-Guided Diversity in Sequential Sampling for Recommender Systems
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911025807228928 |
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| author | Bederina, Hiba Vie, Jill-Jênn |
| author_facet | Bederina, Hiba Vie, Jill-Jênn |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_21617 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bayesian-Guided Diversity in Sequential Sampling for Recommender Systems Bederina, Hiba Vie, Jill-Jênn Information Retrieval Artificial Intelligence 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. |
| title | Bayesian-Guided Diversity in Sequential Sampling for Recommender Systems |
| topic | Information Retrieval Artificial Intelligence |
| url | https://arxiv.org/abs/2506.21617 |