Efficient Graph Collaborative Filtering via Contrastive Learning

Efficient Graph Collaborative Filtering via Contrastive Learning

Collaborative filtering (CF) aims to make recommendations for users by detecting user’s preference from the historical user–item interactions. Existing graph neural networks (GNN) based methods achieve satisfactory performance by exploiting the high-order connectivity between users and items, howeve...

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Bibliographic Details
Main Authors:	Zhiqiang Pan, Honghui Chen
Format:	Article
Language:	English
Published:	MDPI AG 2021-07-01
Series:	Sensors
Subjects:	recommender systems efficient recommendation collaborative filtering graph convolution networks contrastive learning
Online Access:	https://www.mdpi.com/1424-8220/21/14/4666

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