Temporal Recommendation Based on Adaptive Deep Matrix Factorization

Abstract

Temporal recommendation is an important class of tasks in recommender systems, which focuses on modeling and capturing temporal patterns in user behavior to achieve finer-grained and higher-quality recommendations. In real-world scenario, users’ temporal behaviors are not only characterized by sequential dependencies among consecutive items, but also by periodic correlations of different items and time-varying similarity of different users. In this paper, we propose an Adaptive Temporal Recommendation (AdaTR) algorithm to capture the inherent features of temporal behaviors and dynamic collaborative signals. Firstly, based on the periodic characteristics of user behaviors, the user-item interactions are counted and aggregated in different time segments across multiple periods, which forms the temporal user-item interaction matrix. Then, in order to capture the time-varying collaborative signals between different users, a deep spectral clustering (DSC) method is implemented on the temporal user-item interaction matrix, where the original representation of user-item interaction is projected into a latent space, and users’ temporal behaviors are clustered into different groups. Furthermore, an Adaptive Deep Matrix Factorization (AdaDMF) module is designed to learn the time-varying representations of user preferences on each cluster of temporal user behaviors, which incoporate dynamic collaborative signals among different users. Finally, we combine users’ short-term and long-term preferences to generate personalized temporal recommendations. Extensive experiments on four datasets demonstrate that AdaTR performs significantly better than the state-of-the-art baselines.