# Bayesian-Guided Diversity in Sequential Sampling for Recommender Systems

arXiv: https://arxiv.org/abs/2506.21617 · HTML: https://mlsanity.com/paper/2506.21617/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 36 references that are in the map.
- First posted on arXiv 22 Jun 2025.

## Builds on (most cited first, 7 of 7)

- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [A systematic review and taxonomy of explanations in decision support and recommender systems](https://mlsanity.com/paper/2006.08672/) (2020)
- [Fairness in Information Access Systems](https://mlsanity.com/paper/2105.05779/) (2021)
- [Hybrid Recommender Systems: A Systematic Literature Review](https://mlsanity.com/paper/1901.03888/) (2019)
- [A Comprehensive Survey of Evaluation Techniques for Recommendation Systems](https://mlsanity.com/paper/2312.16015/) (2023)
- [Determinantal Point Process Likelihoods for Sequential Recommendation](https://mlsanity.com/paper/2204.11562/) (2022)
- [Generalizing Machine Learning Evaluation through the Integration of Shannon Entropy and Rough Set Theory](https://mlsanity.com/paper/2404.12511/) (2024)

## Built on by (most cited first, 1 of 1)

- [Adaptive Quality-Diversity Trade-offs for Large-Scale Batch Recommendation](https://mlsanity.com/paper/2602.02024/) (2026)

## Abstract (arXiv)

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.