Transcription of Neural Graph Collaborative Filtering - USTC
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Neural Graph Collaborative Filtering - USTC
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.
adopti4, since her similar useru2 has consumedi4 before. Moreover, from the holistic view ofl = 3, itemi4 is more likely to be of interest to u1 than item i5, since there are two paths connecting <i4,u1>, while only one path connects <i5,u1>. Present Work. We propose to model the high-order connectivity information in the embedding function.
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