Counterfactual Multi-player Bandits for Explainable Recommendation Diversification

TL;DR

This paper introduces a counterfactual multi-player bandit approach for explainable recommendation diversification, effectively optimizing various diversity metrics while providing insights into the factors influencing diversity outcomes.

Contribution

It proposes a novel counterfactual multi-player bandit framework that enhances explainability and handles both differentiable and non-differentiable diversity metrics in recommender systems.

Findings

Effective in improving recommendation diversity across datasets

Provides explainability by identifying influencing factors

Works with both differentiable and non-differentiable metrics

Abstract

Existing recommender systems tend to prioritize items closely aligned with users' historical interactions, inevitably trapping users in the dilemma of ``filter bubble''. Recent efforts are dedicated to improving the diversity of recommendations. However, they mainly suffer from two major issues: 1) a lack of explainability, making it difficult for the system designers to understand how diverse recommendations are generated, and 2) limitations to specific metrics, with difficulty in enhancing non-differentiable diversity metrics. To this end, we propose a \textbf{C}ounterfactual \textbf{M}ulti-player \textbf{B}andits (CMB) method to deliver explainable recommendation diversification across a wide range of diversity metrics. Leveraging a counterfactual framework, our method identifies the factors influencing diversity outcomes. Meanwhile, we adopt the multi-player bandits to optimize the counterfactual optimization objective, making it adaptable to both differentiable and non-differentiable diversity metrics. Extensive experiments conducted on three real-world datasets demonstrate the applicability, effectiveness, and explainability of the proposed CMB.