Multi-level Cross-view Contrastive Learning for Knowledge-aware Recommender System

by   Ding Zou, et al.

Knowledge graph (KG) plays an increasingly important role in recommender systems. Recently, graph neural networks (GNNs) based model has gradually become the theme of knowledge-aware recommendation (KGR). However, there is a natural deficiency for GNN-based KGR models, that is, the sparse supervised signal problem, which may make their actual performance drop to some extent. Inspired by the recent success of contrastive learning in mining supervised signals from data itself, in this paper, we focus on exploring the contrastive learning in KG-aware recommendation and propose a novel multi-level cross-view contrastive learning mechanism, named MCCLK. Different from traditional contrastive learning methods which generate two graph views by uniform data augmentation schemes such as corruption or dropping, we comprehensively consider three different graph views for KG-aware recommendation, including global-level structural view, local-level collaborative and semantic views. Specifically, we consider the user-item graph as a collaborative view, the item-entity graph as a semantic view, and the user-item-entity graph as a structural view. MCCLK hence performs contrastive learning across three views on both local and global levels, mining comprehensive graph feature and structure information in a self-supervised manner. Besides, in semantic view, a k-Nearest-Neighbor (kNN) item-item semantic graph construction module is proposed, to capture the important item-item semantic relation which is usually ignored by previous work. Extensive experiments conducted on three benchmark datasets show the superior performance of our proposed method over the state-of-the-arts. The implementations are available at:


page 1

page 2

page 3

page 4


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

Incorporating Knowledge Graphs (KG) into recommeder system has attracted...

LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation

Graph neural network (GNN) is a powerful learning approach for graph-bas...

Multi-level Contrastive Learning Framework for Sequential Recommendation

Sequential recommendation (SR) aims to predict the subsequent behaviors ...

CrossCBR: Cross-view Contrastive Learning for Bundle Recommendation

Bundle recommendation aims to recommend a bundle of related items to use...

Long-tail Augmented Graph Contrastive Learning for Recommendation

Graph Convolutional Networks (GCNs) has demonstrated promising results f...

Knowledge Graph Self-Supervised Rationalization for Recommendation

In this paper, we introduce a new self-supervised rationalization method...

Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation

Recent advancements of sequential deep learning models such as Transform...

Please sign up or login with your details

Forgot password? Click here to reset