Cross-Behavior Feature Regularization and Interaction Contrastive Learning Based Multi-Behavior Recommendation

Multi-behavioral recommendations have become a research hotspot for personalized recommendation tasks. To address the issues of biased information transfer between front and back behaviors and low differentiation between samples in existing multi-behavioral recommendation models, a Cross-Behavior Feature Regularization and Interaction Contrastive Learning based multi-behavior recommendation model (CBFRICL) is proposed. Cross-behavioral feature regularization is used to improve the inter-behavioral feature transfer mechanism based on the differences in pre- and post-behavioral features. Redundant information from previous behaviors is filtered to reduce the enforced dependence between consecutive behaviors. Therefore, the diversity and relative independence of information across different behaviors are preserved. The learning process of the model is constrained using regularization terms. Feature interference is prevented during the transfer process, reducing information distortion and mitigating error accumulation in cascaded Graph Convolutional Network (GCN) structures. An interaction contrastive learning method is proposed based on the spatial distribution rules of user-item interactions. Positive and negative sample pairs are generated using information on user interaction history. Positive samples are brought closer together, and negative samples are pushed away in the feature space, aiding the model in defining the sample boundaries. The sample clustering effect in the feature space is enhanced by capturing the personalized preferences of each user. Thus, the accuracy of the item recommendations is improved. The experimental results show that on the public datasets Tmall, Beibei, and Jdata, CBFRICL improves the HR@K and NDCG@K metrics by a minimum of 4.88% and 1.58%, respectively, compared to the CRGCN model.