Fine-Grained Long and Short-Term Preference Sequential Recommendation with Contrastive Learning

Sequence recommendation aims at item recommendation using users' long and short-term preferences, but most sequence recommendation systems face problems such as insufficient learning power and inadequate fusion of long and short-term preferences. Aiming at the above problems, this paper proposes a fine-grained long and short-term preference sequence recommendation method based on contrastive learning. 1) To address the problem of insufficient long and short-term preference fusion, this paper proposes a long and short-term preference learning layer and a long and short-term preference fusion layer. Firstly, it splits the user behaviour sequence into multi-period sessions and extracts the user's short-term preference in each session by using gated recurrent units, and then fuses the short-term preference sequences to capture the user's long-term preference through the multi-head attention mechanism. Finally, the long-term and short-term preferences are fused adaptively based on the time span to obtain a more representative and comprehensive preference representation. 2) Aiming at the problem of insufficient learning power due to data sparsity, a preference representation comparison learning task is designed to introduce agent user preferences for comparison learning to achieve more accurate preference recommendation. The experimental results show that: compared to the sub-optimal methods, the model improves the Hit@20 metric by 9.84%, 6.40%, and 1.52%, and the MAP@20 metric by 22.64%, 2.42%, and 6.42% on three public datasets, respectively, demonstrating the effectiveness of the proposed method.