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Graph convolution machine for context-aware recommender system |
Jiancan WU1, Xiangnan HE1( ), Xiang WANG2, Qifan WANG3, Weijian CHEN1, Jianxun LIAN4, Xing XIE4 |
1. School of Information Science and Technology, University of Science and Technology of China, Hefei 230026, China
2. 5 Prince George’s Park, National University of Singapore, Singapore 118404, Singapore
3. Google Research, Mountain View, CA 94043, USA
4. Microsoft Research Asia, Beijing 100190, China |
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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 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 the 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.
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| Keywords
context-aware recommender systems
graph convolution
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Corresponding Author(s):
Xiangnan HE
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Just Accepted Date: 25 May 2021
Issue Date: 12 January 2022
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