Matrix factorization (MF) is a classical collaborative filtering algorithm for recommender systems. It decomposes the user-item interaction matrix into a product of low-dimensional user representation matrix and item representation matrix. In typical recommendation scenarios, the user-item interaction paradigm is usually a two-stage process and requires static clustering analysis of the obtained user and item representations. The above process, however, is time and computationally intensive, making it difficult to apply in real-time to e-commerce or Internet of Things environments with billions of users and trillions of items. To address this, we propose a unified matrix factorization method based on dynamic multi-view clustering (MFDMC) that employs an end-to-end training paradigm. Specifically, in each view, a user/item representation is regarded as a weighted projection of all clusters. The representation of each cluster is learnable, enabling the dynamic discarding of bad clusters. Furthermore, we employ multi-view clustering to represent multiple roles of users/items, effectively utilizing the representation space and improving the interpretability of the user/item representations for downstream tasks. Extensive experiments show that our proposed MFDMC achieves state-of-the-art performance on real-world recommendation datasets. Additionally, comprehensive visualization and ablation studies interpretably confirm that our method provides meaningful representations for downstream tasks of users/items.

翻译：矩阵分解（MF）是推荐系统中经典的协同过滤算法，它将用户-物品交互矩阵分解为低维用户表示矩阵与物品表示矩阵的乘积。典型推荐场景中，用户-物品交互范式通常采用两阶段处理流程，需要对获得的用户与物品表示进行静态聚类分析。然而，上述过程的时间复杂度和计算开销较高，难以实时应用于拥有数十亿用户与万亿级物品的电子商务或物联网环境。针对此问题，我们提出一种基于动态多视图聚类的统一矩阵分解方法（MFDMC），采用端到端训练范式。具体而言，在每个视图中，用户/物品表示被视为所有聚类的加权投影。每个聚类表示具有可学习性，能够动态丢弃不良聚类。此外，我们采用多视图聚类来表征用户/物品的多种角色，有效利用表示空间并提升下游任务中用户/物品表示的可解释性。大量实验表明，所提MFDMC方法在真实推荐数据集上取得了最优性能。同时，通过全面的可视化分析与消融实验可解释地证实，本方法可为用户/物品的下游任务提供有意义的表示。