推荐系统
协同过滤
语义学(计算机科学)
光学(聚焦)
知识图
图形
冷启动(汽车)
情报检索
编码
领域(数学)
计算机科学
潜在语义分析
万维网
理论计算机科学
数学
物理
工程类
光学
航空航天工程
化学
程序设计语言
纯数学
基因
生物化学
作者
Ze Wang,Guangyan Lin,Huobin Tan,Qinghong Chen,Xiyang Liu
出处
期刊:International ACM SIGIR Conference on Research and Development in Information Retrieval
日期:2020-07-25
被引量:186
标识
DOI:10.1145/3397271.3401141
摘要
Since it can effectively address the problem of sparsity and cold start of collaborative filtering, knowledge graph (KG) is widely studied and employed as side information in the field of recommender systems. However, most of existing KG-based recommendation methods mainly focus on how to effectively encode the knowledge associations in KG, without highlighting the crucial collaborative signals which are latent in user-item interactions. As such, the learned embeddings underutilize the two kinds of pivotal information and are insufficient to effectively represent the latent semantics of users and items in vector space.
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