UMDSF: Unified Model With Dynamic-Static Features for Personalized Recommendation

Typically, existing works utilize static methods to extract the latent feature representation of user and item reviews, neglecting the time signals and behavior patterns hidden in the user-item interaction history, which may fail to capture users' instant interests and items' temporal attributes. Moreover, there is no framework that unifies recent behavior sequences and reviews. Therefore, in this paper, we first define dynamic and static features to describe users' short- and long-term preferences and items' temporal and inherent attributes. We then design feature extractors to capture these latent factors simultaneously from recent behavior sequences and reviews. Then, we propose a novel unified framework to extract and fuse these fine-grained characteristics, named unified model with dynamic-static features (UMDSF). Specifically, the proposed model extracts both temporal sequence and review features by two parallel feature extractors based on self-attention and a multi-head attention mechanism. Subsequently, an adaptive fusion module is utilized to combine the fine-grained representations for the downstream recommendation tasks. Extensive experiments on four real-world datasets demonstrate the superiority of UMDSF and additional ablation studies verify the effectiveness of the components designed in the proposed model.