Self-supervised collaborative contrast learning for multi-behavior recommendation with adaptive fusion of cross dependency

Jianxing Zheng, Ting Zhang, Suge Wang, Deyu Li, Jian Liao

Abstract: On e-commerce platforms, the multi-behaviors between users and products imply different interests of users for the product. In particular, the intra-behavior dependence and the heterogeneous dependence among group users play an important role in the target behavior decision of users, which can capture the deep interests of users. Previous researches have focused on fusion strategies for final user representations of different behaviors, neglecting adequate modeling of cross dependencies between different behaviors. In this paper, we leverage a self-supervised collaborative contrastive learning framework to learn high-quality user representation for multi-behavior recommendation, named CCLAFMB. The CCLAFMB first designs an adaptive fusion strategy of homogeneous and heterogeneous behaviors and implements their cross dependency propagation process. Then, a self-supervised collaborative contrastive learning paradigm is proposed to ensure the homogeneous and heterogeneous consistency of multi-behavior interest learning. Finally, extensive experimental outcomes on Beibei and Taobao datasets show the proposal achieves improvements of 8.09%, 2.51% on HR@10 metric, and 4.90%, 0.34% on NDCG@10 metric, respectively. The findings demonstrate the significance of adaptive fusion of multi-behavior cross dependencies for multi-behavior recommendation.

External IDs:dblp:journals/apin/ZhengZWLL25