Modeling Multi-Seasonal Multi-Behavior Dependency for Temporal Recommendation
Shichao Liang, Wen Wen, Yali Feng, Ruichu Cai, Zhifeng Hao · ACM Transactions on Intelligent Systems and Technology · 2025
Mining temporal patterns from user behaviors has long been investigated, but most of the existing work centers on single-type user–item interactions, such as purchase or click, which fails to take advantage of the user’s diversified interests revealed by various types of behavior. However, capturing patterns from different behavior sequences and modeling the complex inter-correlation between them are non-trivial tasks, as the high sparsity of type-related interactions, multi-seasonality of individual behaviors, and time-variant dependency of multi-type activities make it really challenging. To address these challenges, we propose a novel framework that aims to model the M ulti-Seasonal M ulti-Behavior Dep endencies (MMDep) both within and across the multi-type behavior sequences. In the proposed model, an item co-occurrence matrix factorization strategy is introduced to alleviate the sparsity issue in type-related behavior sequences. And a temporal dependency module that incorporates multi-scale EMA mechanism is utilized to capture the multi-seasonal dependencies within individual sequences. Moreover, a cross-behavior dependency module is employed to learn the time-variant dependency among different behaviors. Extensive experiments on three real-world datasets demonstrate that the proposed MMDep performs significantly better than the state-of-the-art baselines. And it may provide some new insights and tools on how to leverage multi-behavior data for better temporal recommendation.