Bayesian-Guided Diversity in Sequential Sampling for Recommender Systems

TL;DR

This paper introduces a Bayesian-guided sequential sampling framework that enhances diversity in recommender systems by balancing relevance and content variety through dynamic, multi-objective optimization.

Contribution

It presents a novel Bayesian-based method that adaptively balances diversity and relevance in recommendations using multi-objective, sequential sampling with Pareto optimization.

Findings

Significantly improves diversity in recommendations.

Maintains high relevance while increasing content variety.

Effective in large-scale real-world datasets.

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.