Time-Aware Sequential Recommendation Model Based on Dual-Tower Self-Attention

Abstract

Users’ preferences are migratory and aggregated. Although recommenders have been greatly improved by modeling the timestamps of interactions within a sequential modeling framework, they only consider the time interval of interactions when modeling, making them limited in capturing the temporal dynamics of user prefer-ences. For this reason, this paper proposes a novel time-aware positional embedding that fuses temporal information into the positional embedding to help the network learn item correlations at the temporal level. Then, based on the time-aware positional embedding, this paper proposes a time-aware sequential recommendation model based on dual-tower self-attention (TiDSA). TiDSA includes item-level and feature level self-attention blocks, which analyzes the process of user preference change over time from the perspective of items and features respectively, and achieves the unified modeling of time, items and features. In addition, in the feature-level self-attention block, this paper calculates the self-attention weights from three dimensions, namely, feature-feature, item-item and item-feature, to fully capture the correlation between different features. Finally, the model fuses the item-level and feature-level information to obtain the final user preference representation and provides reliable recommendation results for users. Experimental results on four real-world datasets show that TiDSA outperforms various state-of-the-art models.

Keywords

• time-aware sequential recommendation; positional embedding; feature-level self-attention; dual-tower self-attention