Recommending items to potentially interested users has been an important commercial task that faces two main challenges: accuracy and explainability. While most collaborative filtering models rely on statistical computations on a large scale of interaction data between users and items and can achieve high performance, they often lack clear explanatory power. We propose UIPC-MF, a prototype-based matrix factorization method for explainable collaborative filtering recommendations. In UIPC-MF, both users and items are associated with sets of prototypes, capturing general collaborative attributes. To enhance explainability, UIPC-MF learns connection weights that reflect the associative relations between user and item prototypes for recommendations. UIPC-MF outperforms other prototype-based baseline methods in terms of Hit Ratio and Normalized Discounted Cumulative Gain on three datasets, while also providing better transparency.

翻译：为潜在感兴趣用户推荐商品是一项重要的商业任务，面临准确性和可解释性两大挑战。尽管大多数协同过滤模型依赖对用户与项目间大规模交互数据的统计计算并能够取得高性能表现，但它们往往缺乏清晰的解释能力。我们提出UIPC-MF，一种基于原型的矩阵分解方法，用于可解释的协同过滤推荐。在UIPC-MF中，用户和项目均与原型集合相关联，以捕获通用协同属性。为增强可解释性，UIPC-MF学习反映用户与项目原型间关联关系的连接权重以进行推荐。在三个数据集上，UIPC-MF在命中率和归一化折损累计增益指标上优于其他基于原型的基线方法，同时提供了更好的透明度。