Contrastive multi-interest graph attention network for knowledge-aware recommendation

Abstract

Acquiring high-quality representations for both users and items is essential, facilitating a wide range of recommendation scenarios. Utilizing graph neural networks for knowledge-aware recommendation is a recent trend. However, there are two deficiencies in existing GNN-based knowledge-aware models: (1) They are coarse-grained in user representation, failing to capture the multi-interest distribution of users. (2) The supervised signals come only from historical interactions, failing to provide high-quality representations due to sparse data. In this paper, we propose a novel model, CMGAN with Contrastive Multi-interest Graph Attention Network, tailored for personalized knowledge-aware recommendations. Specifically, CMGAN employs a collaborative knowledge graph encoder, enhancing node representations through relational-aware embedding aggregation. Then a dynamic multi-interest generator crafts fine-grained multi-interest representations, adeptly extracting varied interests for each user based on their historical interactions. Furthermore, CMGAN innovates by integrating multi-level contrastive learning to refine representations at both node and multi-interest granularity. It consists of collaborative knowledge graph contrastive learning and multi-interest contrastive learning. The former pursues the acquisition of node representations that are more uniformly distributed, while the latter aims to obtain interest representations that are more distinct. A series of experiments on three benchmark datasets indicate that our model surpasses current state-of-the-art models. The code can be obtainable at https://github.com/liujianfang2021/CMGAN.