时间感知的双塔型自注意力序列推荐模型OACSTPCD

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.