Please use this identifier to cite or link to this item: https://doi.org/10.1145/3331184.3331267
Title: Neural Graph Collaborative Filtering
Authors: Xiang Wang 
Xiangnan He 
Meng Wang 
Fuli Feng 
Tat-Seng Chua 
Keywords: Collaborative Filtering
Embedding Propagation
Graph Neural Network
High-order Connectivity
Recommendation
Issue Date: 21-Jul-2019
Publisher: Association for Computing Machinery, Inc
Citation: Xiang 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
Abstract: Learning 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.
Source Title: SIGIR 2019
URI: https://scholarbank.nus.edu.sg/handle/10635/167285
ISBN: 9781450361729
DOI: 10.1145/3331184.3331267
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