结合对比学习的细粒度长短期偏好序列推荐OA

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 prefer-ence 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 adap-tively 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 experi-mental 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 effec-tiveness of the proposed method.