用反事实多玩家博弈提升推荐多样性并解释原因
Counterfactual Multi-player Bandits for Explainable Recommendation Diversification
- 基于反事实框架分析影响多样性的关键因素
- 在三个真实数据集上验证方法有效且可解释
- 适用于可导与不可导的多样性指标，通用性强
现有推荐系统倾向于优先推送用户历史行为相关的内容，易造成信息茧房。尽管近期研究致力于提升推荐多样性，但仍存在两大问题：一是缺乏可解释性，难以理解推荐如何生成；二是局限于特定度量指标，难以优化不可导的多样性指标。为此，我们提出一种反事实多玩家博弈（CMB）方法，实现跨多种多样性指标的可解释推荐多样化。通过反事实框架识别影响多样性结果的关键因素，并利用多玩家博弈优化反事实目标，使方法适用于可导与不可导的多样性指标。在三个真实数据集上的大量实验表明，所提CMB方法具有广泛适用性、有效性及可解释性。
原文摘要 · Abstract (English)
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.
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