Graph Contrastive Learning With Negative Propagation for Recommendation

Abstract

Previous recommendation models build interest embeddings heavily relying on the observed interactions and optimize the embeddings with a contrast between the interactions and randomly sampled negative instances. To our knowledge, the negative interest signals remain unexplored in interest encoding, which merely serves losses for backpropagation. Besides, the sparse undifferentiated interactions inherently bring implicit bias in revealing users’ interests, leading to suboptimal interest prediction. The negative interest signals would be a piece of promising evidence to support detailed interest modeling. In this work, we propose a perturbed graph contrastive learning with negative propagation (PCNP) for recommendation, which introduces negative interest to assist interest modeling in a contrastive learning (CL) architecture. An auxiliary channel of negative interest learning generates a contrastive graph by negative sampling and propagates complementary embeddings of users and items to encode negative signals. The proposed PCNP contrasts positive and negative embeddings to promote interest modeling for recommendation. Extensive experiments demonstrate the capability of PCNP using two-level CL to alleviate interaction sparsity and bias issues for recommendation.