A Bayesian matrix factorization model for dynamic user embedding in recommender system  

作　　者：Kaihan ZHANG Zhiqiang WANG Jiye LIANG Xingwang ZHAO 

机构地区：[1]Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education,School of Computer and Information Technology,Shanxi University,Taiyuan 030006,China

出　　处：《Frontiers of Computer Science》2022年第5期233-235,共3页中国计算机科学前沿（英文版）

基　　金：This work was supported by the National Natural Science Foundation of China(Grant Nos.U21A20473,61906111,and 72171137);the Projects of Key Research and Development Plan of Shanxi Province(201903D121162);the 1331 Engineering Project of Shanxi Province.

摘　　要：1 Introduction The main idea of recommender system is how to learn accurate users’embeddings from behavior data[1].Each dimension of users’embeddings can reflect the interests of users in different potential aspects.In real-world scenarios,users’interests are drifting over time,which brings a challenge to learn accurate dynamic users’embeddings.Recently,various time-aware recommendation methods have been proposed to tackle this problem by modeling the evolution process of users’interests[2−4].However,they usually assume that users’embeddings drift with the same range on all dimensions.In practice,users’embeddings should change diversely on different dimensions over time.Specifically,for the rapidly changing interests of the users,the corresponding dimensions should change significantly.On the contrary,the dimensions representing stable interests may change slightly.

关 键 词：DIMENSIONS system DYNAMIC 

分 类 号：O15[理学—数学]