Leveraging implicit social structures for recommendation via a Bayesian generative model  

作　　者：Huafeng LIU Jingxuan WEN Liping JING Jian YU 

机构地区：[1]School of Computer and Information Technology,Beijing Jiaotong University,Beijing 100044,China [2]Beijing Key Lab of Traffic Data Analysis and Mining,Beijing Jiaotong University,Beijing 100044,China

出　　处：《Science China(Information Sciences)》2022年第4期265-267,共3页中国科学（信息科学）（英文版）

基　　金：supported in part by National Natural Science Foundation of China(Grant Nos.61822601,61773050,61632004);Beijing Natural Science Foundation(Grant No.Z180006);National Key Research and Development Program(Grant No.2017YFC1703506);Fundamental Research Funds for the Central Universities(Grant No.2019JBZ110).

摘　　要：Dear editor,Social recommendation leverages the social relations between users and their past behaviors to model user preferences.While social recommendation is a core component in recommendation systems,discovering and utilizing hidden patterns among complex social networks and user behavior data is not a trivial problem.This study focuses on the cold-start problem of recommendation systems and attempts to solve it with the aid of social relations.Thus,we propose a new matrix approximation model by leveraging implicit social structures for recommendation(SoMA).SoMA takes advantage of social networks and user preference information via a joint graphical model to determine latent user and item factors.

关 键 词：IMPLICIT utilizing STRUCTURES 

分 类 号：TP391.3[自动化与计算机技术—计算机应用技术]