文献类型：期刊文献

英文题名：INTEGRATING RATING INFORMATION AND SOCIAL INFORMATION FOR COLLABORATIVE FILTERING RECOMMENDATION

作者：Mu, Ruihui[1]

第一作者：穆瑞辉

通讯作者：Mu, RH[1]

机构：[1]Xinxiang Univ, Coll Comp & Informat Engn, Xinxiang 453003, Henan, Peoples R China

第一机构：新乡学院计算机与信息工程学院

通讯机构：[1]corresponding author), Xinxiang Univ, Coll Comp & Informat Engn, Xinxiang 453003, Henan, Peoples R China.|[1107118]新乡学院计算机与信息工程学院;[11071]新乡学院;

年份：2019

卷号：72

期号：5

起止页码：584-+

外文期刊名：COMPTES RENDUS DE L ACADEMIE BULGARE DES SCIENCES

收录：;Scopus(收录号：2-s2.0-85071031536);WOS:【SCI-EXPANDED(收录号:WOS:000474480800003)】；

基金：This research was partially supported by the National Key Research and Development Plan Key Projects of China under Grant no. 2017YFC0405800, the National Natural Science Foundation of China (Grant nos 60971088, 60571048, 61432008, and 61375121).

语种：英文

外文关键词：collaborative filtering; social information; recommendation algorithm; rating information

摘要：In order to address the problem of cold start and data sparsity, we proposed a novel collaborative filtering recommendation algorithm based on social information by integrating the user's social information and rating information. Experimental results show that the proposed algorithm can effectively alleviate the user cold start problem in the collaborative filtering recommendation algorithm and is superior to the state-of-the-art algorithms.