Efficient Graph Collaborative Filtering via Contrastive Learning.

Zhiqiang Pan,Honghui Chen

doi:10.3390/s21144666

Abstract

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, however they suffer from the poor training efficiency problem and easily introduce bias for information propagation. Moreover, the widely applied Bayesian personalized ranking (BPR) loss is insufficient to provide supervision signals for training due to the extremely sparse observed interactions. To deal with the above issues, we propose the Efficient Graph Collaborative Filtering (EGCF) method. Specifically, EGCF adopts merely one-layer graph convolution to model the collaborative signal for users and items from the first-order neighbors in the user–item interactions. Moreover, we introduce contrastive learning to enhance the representation learning of users and items by deriving the self-supervisions, which is jointly trained with the supervised learning. Extensive experiments are conducted on two benchmark datasets, i.e., Yelp2018 and Amazon-book, and the experimental results demonstrate that EGCF can achieve the state-of-the-art performance in terms of Recall and normalized discounted cumulative gain (NDCG), especially on ranking the target items at right positions. In addition, EGCF shows obvious advantages in the training efficiency compared with the competitive baselines, making it practicable for potential applications.

Highlights

Recommender systems can help provide users with personalized information from the explosively increasing information on the internet [1,2,3], which have been widely applied in web search, e-commerce websites, etc. [4,5]
Comparing GRMF to MF, we can observe that GRMF outperforms MF obviously in terms of Recall@20 and normalized discounted cumulative gain (NDCG)@20 on both two datasets, indicating the effectiveness of the graph Laplacian regularizer on smoothing the matrix factorization
In order to provide more detailed analysis on the impact of α on the model performance, we evaluate the performance of Efficient Graph Collaborative Filtering (EGCF) by tuning α in {0.005, 0.01, 0.05, 0.1, 0.5} and fixing λ as 8, the training curves in terms of Recall@20 on two datasets are provided in Figure 4, similar phenomenons can be observed on the NDCG@20 metric

Summary

Introduction

Recommender systems can help provide users with personalized information from the explosively increasing information on the internet [1,2,3], which have been widely applied in web search, e-commerce websites, etc. [4,5]. Recommender systems can help provide users with personalized information from the explosively increasing information on the internet [1,2,3], which have been widely applied in web search, e-commerce websites, etc. The core of a personalized recommender is to accurately capture the user preference from her historical interactions with items [2,6]. Collaborative filtering (CF) aims to learn accurate representation of users and items by reconstructing the user–item interaction matrix, item recommendations can be generated by ranking the items according to the learnt user and item latent factors [1,7]

Methods

Results

Conclusion