协同过滤
邻接矩阵
二部图
计算机科学
推荐系统
范围(计算机科学)
图形
邻接表
极限(数学)
情报检索
降噪
理论计算机科学
人工智能
算法
数学
数学分析
程序设计语言
作者
Ziwei Fan,Ke Xu,Zhili Dong,Hao Peng,Jiawei Zhang,Philip S. Yu
标识
DOI:10.1145/3539618.3591994
摘要
Graph collaborative filtering (GCF) is a popular technique for capturing high-order collaborative signals in recommendation systems. However, GCF's bipartite adjacency matrix, which defines the neighbors being aggregated based on user-item interactions, can be noisy for users/items with abundant interactions and insufficient for users/items with scarce interactions. Additionally, the adjacency matrix ignores user-user and item-item correlations, which can limit the scope of beneficial neighbors being aggregated.
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