杠杆(统计)
计算机科学
利用
图形
协同过滤
人工智能
构造(python库)
机器学习
理论计算机科学
推荐系统
计算机安全
程序设计语言
作者
Guanghui Zhu,W. T. Lu,Chunfeng Yuan,Yihua Huang
标识
DOI:10.1145/3539618.3591632
摘要
Graph collaborative filtering has achieved great success in capturing users' preferences over items. Despite effectiveness, graph neural network (GNN)-based methods suffer from data sparsity in real scenarios. Recently, contrastive learning (CL) has been used to address the problem of data sparsity. However, most CL-based methods only leverage the original user-item interaction graph to construct the CL task, lacking the explicit exploitation of the higher-order information (i.e., user-user and item-item relationships). Even for the CL-based method that uses the higher-order information, the reception field of the higher-order information is fixed and regardless of the difference between nodes. In this paper, we propose a novel adaptive multi-view fusion contrastive learning framework, named AdaMCL, for graph collaborative filtering. To exploit the higher-order information more accurately, we propose an adaptive fusion strategy to fuse the embeddings learned from the user-item and user-user graphs. Moreover, we propose a multi-view fusion contrastive learning paradigm to construct effective CL tasks. Besides, to alleviate the noisy information caused by aggregating higher-order neighbors, we propose a layer-level CL task. Extensive experimental results reveal that AdaMCL is effective and outperforms existing collaborative filtering models significantly.
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