Unified Matrix Factorization with Dynamic Multi-view Clustering

TL;DR

This paper introduces a unified matrix factorization approach with dynamic multi-view clustering that enhances real-time recommendation accuracy and interpretability by adaptively discarding poor clusters and modeling multiple user/item roles.

Contribution

It proposes a novel end-to-end training framework for matrix factorization that incorporates dynamic multi-view clustering to improve efficiency and representation quality in recommender systems.

Findings

Achieves state-of-the-art performance on real-world datasets.

Provides interpretable user/item representations.

Effectively models multiple roles of users/items.

Abstract

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.