Please use this identifier to cite or link to this item: https://doi.org/10.1145/3331184.3331267
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dc.titleNeural Graph Collaborative Filtering
dc.contributor.authorXiang Wang
dc.contributor.authorXiangnan He
dc.contributor.authorMeng Wang
dc.contributor.authorFuli Feng
dc.contributor.authorTat-Seng Chua
dc.date.accessioned2020-04-28T02:08:01Z
dc.date.available2020-04-28T02:08:01Z
dc.date.issued2019-07-21
dc.identifier.citationXiang Wang, Xiangnan He, Meng Wang, Fuli Feng, Tat-Seng Chua (2019-07-21). Neural Graph Collaborative Filtering. SIGIR 2019 : 165-174. ScholarBank@NUS Repository. https://doi.org/10.1145/3331184.3331267
dc.identifier.isbn9781450361729
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/167285
dc.description.abstractLearning vector representations (aka. embeddings) of users and items lies at the core of modern recommender systems. Ranging from early matrix factorization to recently emerged deep learning based methods, existing efforts typically obtain a user's (or an item's) embedding by mapping from pre-existing features that describe the user (or the item), such as ID and attributes. We argue that an inherent drawback of such methods is that, the collaborative signal, which is latent in user-item interactions, is not encoded in the embedding process. As such, the resultant embeddings may not be sufficient to capture the collaborative filtering effect. In this work, we propose to integrate the user-item interactions - more specifically the bipartite graph structure - into the embedding process. We develop a new recommendation framework Neural Graph Collaborative Filtering (NGCF), which exploits the user-item graph structure by propagating embeddings on it. This leads to the expressive modeling of high-order connectivity in user-item graph, effectively injecting the collaborative signal into the embedding process in an explicit manner. We conduct extensive experiments on three public benchmarks, demonstrating significant improvements over several state-of-the-art models like HOP-Rec [39] and Collaborative Memory Network [5]. Further analysis verifies the importance of embedding propagation for learning better user and item representations, justifying the rationality and effectiveness of NGCF. Codes are available at https://github.com/xiangwang1223/neural_graph_collaborative_filtering. © 2019 Association for Computing Machinery.
dc.publisherAssociation for Computing Machinery, Inc
dc.subjectCollaborative Filtering
dc.subjectEmbedding Propagation
dc.subjectGraph Neural Network
dc.subjectHigh-order Connectivity
dc.subjectRecommendation
dc.typeConference Paper
dc.contributor.departmentDEPT OF COMPUTER SCIENCE
dc.description.doi10.1145/3331184.3331267
dc.description.sourcetitleSIGIR 2019
dc.description.page165-174
dc.grant.idR-252-300-002-490
dc.grant.fundingagencyInfocomm Media Development Authority
dc.grant.fundingagencyNational Research Foundation
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