Graph Convolution Machine for Context-aware Recommender System

Abstract

The latest advance in recommendation shows that better user and item representations can be learned via performing graph convolutions on the user-item interaction graph. However, such finding is mostly restricted to the collaborative filtering (CF) scenario, where the interaction contexts are not available. In this work, we extend the advantages of graph convolutions to context-aware recommender system (CARS, which represents a generic type of models that can handle various side information). We propose \textit{Graph Convolution Machine} (GCM), an end-to-end framework that consists of three components: an encoder, graph convolution (GC) layers, and a decoder. The encoder projects users, items, and contexts into embedding vectors, which are passed to the GC layers that refine user and item embeddings with context-aware graph convolutions on user-item graph. The decoder digests the refined embeddings to output the prediction score by considering the interactions among user, item, and context embeddings. We conduct experiments on three real-world datasets from Yelp and Amazon, validating the effectiveness of GCM and the benefits of performing graph convolutions for CARS. Our implementations are available at \url{https://github.com/wujcan/GCM}.

arXiv comment19 pages, 4 figures, 4 tables. Accepted by Frontiers of Computer Science

References in corpus (15)

• Adam: A Method for Stochastic Optimization
• Inductive Representation Learning on Large Graphs
• BPR: Bayesian Personalized Ranking from Implicit Feedback
• Neural Graph Collaborative Filtering
• Graph Convolutional Neural Networks for Web-Scale Recommender Systems
• KGAT: Knowledge Graph Attention Network for Recommendation
• Session-based Recommendation with Graph Neural Networks
• Graph Convolutional Matrix Completion
• xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems
• Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferences
• Session-based Social Recommendation via Dynamic Graph Attention Networks
• LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation
• Fi-GNN: Modeling Feature Interactions via Graph Neural Networks for CTR Prediction
• Spectral Collaborative Filtering
• Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction

Cited by in corpus (4)

• On the Effectiveness of Sampled Softmax Loss for Item Recommendation
• Graph-Segmenter: Graph Transformer with Boundary-aware Attention for Semantic Segmentation
• Graph Neural Pre-training for Enhancing Recommendations using Side Information
• Intrinsic and Extrinsic Factor Disentanglement for Recommendation in Various Context Scenarios