Collaborative Filtering (CF) is a widely used and effective technique for recommender systems. In recent decades, there have been significant advancements in latent embedding-based CF methods for improved accuracy, such as matrix factorization, neural collaborative filtering, and LightGCN. However, the explainability of these models has not been fully explored. Adding explainability to recommendation models can not only increase trust in the decisionmaking process, but also have multiple benefits such as providing persuasive explanations for item recommendations, creating explicit profiles for users and items, and assisting item producers in design improvements. In this paper, we propose a neat and effective Explainable Collaborative Filtering (ECF) model that leverages interpretable cluster learning to achieve the two most demanding objectives: (1) Precise - the model should not compromise accuracy in the pursuit of explainability; and (2) Self-explainable - the model's explanations should truly reflect its decision-making process, not generated from post-hoc methods. The core of ECF is mining taste clusters from user-item interactions and item profiles.We map each user and item to a sparse set of taste clusters, and taste clusters are distinguished by a few representative tags. The user-item preference, users/items' cluster affiliations, and the generation of taste clusters are jointly optimized in an end-to-end manner. Additionally, we introduce a forest mechanism to ensure the model's accuracy, explainability, and diversity. To comprehensively evaluate the explainability quality of taste clusters, we design several quantitative metrics, including in-cluster item coverage, tag utilization, silhouette, and informativeness. Our model's effectiveness is demonstrated through extensive experiments on three real-world datasets.

翻译：协同过滤（CF）是推荐系统中广泛使用且有效的技术。近几十年来，基于潜在嵌入的CF方法（如矩阵分解、神经协同过滤和LightGCN）在提升准确性方面取得了显著进展。然而，这些模型的可解释性尚未得到充分探索。为推荐模型增加可解释性不仅能提升对决策过程的信任，还能带来多重益处，例如为物品推荐提供有说服力的解释、为用户和物品构建显式画像，以及辅助物品生产者改进设计。本文提出一种简洁而有效的可解释协同过滤（ECF）模型，通过利用可解释的聚类学习实现两个最核心的目标：（1）精确性——模型在追求可解释性的同时不牺牲准确性；（2）自解释性——模型的解释需真实反映其决策过程，而非通过后处理方法生成。ECF的核心是从用户-物品交互和物品画像中挖掘品味聚类。我们将每个用户和物品映射到一组稀疏的品味聚类上，并通过少量代表性标签区分不同品味聚类。用户-物品偏好、用户/物品的聚类归属以及品味聚类的生成以端到端方式联合优化。此外，我们引入森林机制以确保模型的准确性、可解释性和多样性。为全面评估品味聚类的可解释性质量，我们设计了多项定量指标，包括簇内物品覆盖率、标签利用率、轮廓系数和信息量。通过在三个真实数据集上的大量实验，验证了模型的有效性。