Improving Knowledge-aware Recommendation with Multi-level Interactive Contrastive Learning

Abstract

Incorporating Knowledge Graphs (KG) into recommeder system has attracted considerable attention. Recently, the technical trend of Knowledge-aware Recommendation (KGR) is to develop end-to-end models based on graph neural networks (GNNs). However, the extremely sparse user-item interactions significantly degrade the performance of the GNN-based models, as: 1) the sparse interaction, means inadequate supervision signals and limits the supervised GNN-based models; 2) the combination of sparse interactions (CF part) and redundant KG facts (KG part) results in an unbalanced information utilization. Besides, the GNN paradigm aggregates local neighbors for node representation learning, while ignoring the non-local KG facts and making the knowledge extraction insufficient. Inspired by the recent success of contrastive learning in mining supervised signals from data itself, in this paper, we focus on exploring contrastive learning in KGR and propose a novel multi-level interactive contrastive learning mechanism. Different from traditional contrastive learning methods which contrast nodes of two generated graph views, interactive contrastive mechanism conducts layer-wise self-supervised learning by contrasting layers of different parts within graphs, which is also an "interaction" action. Specifically, we first construct local and non-local graphs for user/item in KG, exploring more KG facts for KGR. Then an intra-graph level interactive contrastive learning is performed within each graph, which contrasts layers of the CF and KG parts, for more consistent information leveraging. Besides, an inter-graph level interactive contrastive learning is performed between the local and non-local graphs, for sufficiently and coherently extracting non-local KG signals. Extensive experiments conducted on three benchmark datasets show the superior performance of our proposed method over the state-of-the-arts.

arXiv commentAccepted to CIKM 2022

References in corpus (22)

• Adam: A Method for Stochastic Optimization
• A Simple Framework for Contrastive Learning of Visual Representations
• BPR: Bayesian Personalized Ranking from Implicit Feedback
• Graph Convolutional Neural Networks for Web-Scale Recommender Systems
• KGAT: Knowledge Graph Attention Network for Recommendation
• Self-supervised Graph Learning for Recommendation
• RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems
• xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems
• Knowledge Graph Convolutional Networks for Recommender Systems
• Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferences
• Learning Intents behind Interactions with Knowledge Graph for Recommendation
• Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning
• Global Context Enhanced Graph Neural Networks for Session-based Recommendation
• Deep Graph Contrastive Representation Learning
• Contrastive Multi-View Representation Learning on Graphs
• SHINE: Signed Heterogeneous Information Network Embedding for Sentiment Link Prediction
• Multi-level Cross-view Contrastive Learning for Knowledge-aware Recommender System
• DKN: Deep Knowledge-Aware Network for News Recommendation
• Multi-view Intent Disentangle Graph Networks for Bundle Recommendation
• Are Graph Augmentations Necessary? Simple Graph Contrastive Learning for Recommendation
• Hierarchical Attentive Knowledge Graph Embedding for Personalized Recommendation
• Less is More: Reweighting Important Spectral Graph Features for Recommendation

Cited by in corpus (5)

• Heterogeneous Graph Contrastive Learning for Recommendation
• Disentangled Contrastive Collaborative Filtering
• VRKG4Rec: Virtual Relational Knowledge Graphs for Recommendation
• Knowledge Enhanced Multi-intent Transformer Network for Recommendation
• A Comprehensive Survey on Self-Supervised Learning for Recommendation