Abstract: The sequence recommendation algorithm based on self-attention mechanism utilizes users’ interactive sequences to model their dynamic preferences and predict their future behaviors. However, directly inputting the interactive sequences into the self-attention layer will limit the effective utilization of local association information in the sequences. In addition, most of the existing recommendation algorithms use the dot product of the representation of the users’ recent behaviors and the target items to calculate the item scores, which will weaken the impact of previous interactive items on the recommendation results. This paper proposes a date-aware sequential recommendation algorithm that fuses local information of sequences. It uses multiple vertical filters to fuse multiple local association information of each interactive item in the interactive sequence, and uses cross-attention mechanism to capture the relationships between all historical interactive items and target items. It also abandons the traditional position embedding method and replaces it with absolute time embedding based on the date of inter-action occurrence. Experimental results on multiple public datasets show that the algorithm has certain improvement compared with the baseline algorithms in different evaluation indicators.

Key words: sequence recommendation, convolutional neural network, attention mechanism