Knowledge Graph Convolutional Networks for Recommender Systems

Abstract

To alleviate sparsity and cold start problem of collaborative filtering based recommender systems, researchers and engineers usually collect attributes of users and items, and design delicate algorithms to exploit these additional information. In general, the attributes are not isolated but connected with each other, which forms a knowledge graph (KG). In this paper, we propose Knowledge Graph Convolutional Networks (KGCN), an end-to-end framework that captures inter-item relatedness effectively by mining their associated attributes on the KG. To automatically discover both high-order structure information and semantic information of the KG, we sample from the neighbors for each entity in the KG as their receptive field, then combine neighborhood information with bias when calculating the representation of a given entity. The receptive field can be extended to multiple hops away to model high-order proximity information and capture users' potential long-distance interests. Moreover, we implement the proposed KGCN in a minibatch fashion, which enables our model to operate on large datasets and KGs. We apply the proposed model to three datasets about movie, book, and music recommendation, and experiment results demonstrate that our approach outperforms strong recommender baselines.

Proceedings of the 2019 World Wide Web Conference

References in corpus (6)

• Inductive Representation Learning on Large Graphs
• Graph Convolutional Neural Networks for Web-Scale Recommender Systems
• RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems
• Convolutional Networks on Graphs for Learning Molecular Fingerprints
• SHINE: Signed Heterogeneous Information Network Embedding for Sentiment Link Prediction
• Joint Topic-Semantic-aware Social Recommendation for Online Voting

Cited by in corpus (43)

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• MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding
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• Multi-level Cross-view Contrastive Learning for Knowledge-aware Recommender System
• Research Commentary on Recommendations with Side Information: A Survey and Research Directions
• Incorporating Domain Knowledge into Deep Neural Networks
• A Review of Some Techniques for Inclusion of Domain-Knowledge into Deep Neural Networks
• Knowledge Graph Self-Supervised Rationalization for Recommendation
• DGCN: Diversified Recommendation with Graph Convolutional Networks
• MetaKG: Meta-learning on Knowledge Graph for Cold-start Recommendation
• Comprehensive evaluation of deep and graph learning on drug-drug interactions prediction
• Improving Knowledge-aware Recommendation with Multi-level Interactive Contrastive Learning
• Solving Cold Start Problem in Recommendation with Attribute Graph Neural Networks
• Multi-behavior Self-supervised Learning for Recommendation
• ATBRG: Adaptive Target-Behavior Relational Graph Network for Effective Recommendation
• Joint Multi-grained Popularity-aware Graph Convolution Collaborative Filtering for Recommendation
• Recommender systems based on graph embedding techniques: A comprehensive review
• Knowledge-guided Deep Reinforcement Learning for Interactive Recommendation
• Knowledge-refined Denoising Network for Robust Recommendation
• paper2repo: GitHub Repository Recommendation for Academic Papers
• Ripple Knowledge Graph Convolutional Networks For Recommendation Systems
• USER: A Unified Information Search and Recommendation Model based on Integrated Behavior Sequence
• DaisyRec 2.0: Benchmarking Recommendation for Rigorous Evaluation
• Knowledge Enhanced Multi-intent Transformer Network for Recommendation
• Inclusion of Domain-Knowledge into GNNs using Mode-Directed Inverse Entailment
• Comprehending Knowledge Graphs with Large Language Models for Recommender Systems
• Recommending on graphs: a comprehensive review from a data perspective
• A Light Heterogeneous Graph Collaborative Filtering Model using Textual Information
• NudgeRank: Digital Algorithmic Nudging for Personalized Health
• FedRKG: A Privacy-preserving Federated Recommendation Framework via Knowledge Graph Enhancement
• LightKG: Efficient Knowledge-Aware Recommendations with Simplified GNN Architecture
• RecKG: Knowledge Graph for Recommender Systems
• Constant Time Graph Neural Networks
• LogicRec: Recommendation with Users' Logical Requirements
• SAppKG: Mobile App Recommendation Using Knowledge Graph and Side Information-A Secure Framework
• A Knowledge Graph based Approach for Mobile Application Recommendation
• KG4RecEval: Does Knowledge Graph Really Matter for Recommender Systems?
• Att-KGCN: Tourist Attractions Recommendation System by using Attention mechanism and Knowledge Graph Convolution Network
• printf: Preference Modeling Based on User Reviews with Item Images and Textual Information via Graph Learning
• SPARK: Adaptive Low-Rank Knowledge Graph Modeling in Hybrid Geometric Spaces for Recommendation
• Knowledge Graphs Meet Graph Neural Networks: A Comprehensive Survey