Convolutional Memory Graph Collaborative Filtering

doi:10.13190/j.jbupt.2020-226

Journal of Beijing University of Posts and Telecommunications ›› 2021, Vol. 44 ›› Issue (3): 21-26.doi: 10.13190/j.jbupt.2020-226

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Convolutional Memory Graph Collaborative Filtering

LIU Guo-zhen, CHEN Hong-long

College of Control Science and Engineering, China University of Petroleum(East China), Qingdao 266580, China

Received:2020-11-03 Online:2021-06-28 Published:2021-06-23

Abstract

Abstract: An end-to-end graph neural networks with memory unit is proposed for user vector representations and items in recommender systems. Gated recurrent unit is introduced to reduce the information loss between high-order connected nodes. This enables users and items nodes to obtain more complete feature information from high-order neighbor nodes. The convolutional neural networks are used to fuse feature vectors between different output layers to obtain users' preferences at different stages. Experiments on 4 datasets show that compared with the optimal comparison algorithms, the performance of proposed algorithm achieves gain of 1.98%, 4.17%, 9.27% and 2.7%, respectively.

Key words: graph neural networks, gated recurrent unit, convolutional neural networks, rating prediction, recommender systems

CLC Number:

TP301

LIU Guo-zhen, CHEN Hong-long. Convolutional Memory Graph Collaborative Filtering[J]. Journal of Beijing University of Posts and Telecommunications, 2021, 44(3): 21-26.

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Convolutional Memory Graph Collaborative Filtering

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