计算机科学
推荐系统
语义学(计算机科学)
图形
人工智能
机器学习
理论计算机科学
程序设计语言
作者
Bowen Jin,Chen Gao,Xiangnan He,Depeng Jin,Yong Li
标识
DOI:10.1145/3397271.3401072
摘要
Traditional recommendation models that usually utilize only one type of user-item interaction are faced with serious data sparsity or cold start issues. Multi-behavior recommendation taking use of multiple types of user-item interactions, such as clicks and favorites, can serve as an effective solution. Early efforts towards multi-behavior recommendation fail to capture behaviors' different influence strength on target behavior. They also ignore behaviors' semantics which is implied in multi-behavior data. Both of these two limitations make the data not fully exploited for improving the recommendation performance on the target behavior.
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