Transcription of Neural Graph Collaborative Filtering
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Neural Graph Collaborative Filtering
Neural Graph Collaborative FilteringXiang WangNational University of He University of Science and Technologyof WangHefei University of FengNational University of ChuaNational University of vector representations ( ) of users anditems lies at the core of modern recommender systems. Rangingfrom early matrix factorization to recently emerged deep learningbased methods, existing efforts typically obtain a user s (or anitem s) embedding by mapping from pre-existing features thatdescribe the user (or the item), such as ID and attributes. Weargue that an inherent drawback of such methods is that, thecollaborative signal, which is latent in user-item interactions,is not encoded in the embedding process. As such, the resultantembeddings may not be sufficient to capture the collaborativefiltering this work, we propose to integrate the user-item interactions more specifically the bipartite Graph structure into the embeddingprocess.
CCS CONCEPTS • Information systems → Recommender systems. KEYWORDS Collaborative Filtering, Recommendation, High-order Connectivity, Embedding Propagation, Graph Neural Network ∗Xiangnan He is the corresponding author. Permission to make digital or hard copies of all or part of this work for personal or
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