Hybrid recommendation algorithm based on multi-attribute rating from online reviews

摘要：

　　As the growing users and products in network shopping platform under the big data environment,the data sparsity and cold start problem are increasingly prominent which lead to the recommended effect of recommendation algorithm can't be satisfied by users.For this problem,the paper presents a construction method of user preference model and product feature model based on information mining of online reviews,and then it eases data sparsity through multi-attribute rating.And the paper solves the problem of user cold start and product cold start to a certain extent through the algorithm of similarity which is based user attributes and product attributes.Finally,the paper combines with multiple similarity algorithms to construct hybrid recommendation algorithm based on user preference and product feature.Simulation experiments verify the ability to solve the cold start problem and good recommendation accuracy of the algorithm through collecting 10000 online reviews information from the mobile channel of Amazon.

关键词： Hybrid recommendation Multi-attribute rating Online reviews User preference Product feature

作者: Jinhai Li Zhunan Qian Peng Zhang Youshi He

作者单位: Taizhou University,Taizhou 225300,China School of Management,Jiangsu University,Zhenjiang,212013

会议类型: 国际会议

会议名称: 2019国图灵大会(ACM Turing Celebration conference-China 2019 )

会议地点: 成都

会议语种:英文

页码: 861-867

在线出版日期: 2019-05-17（万方平台首次上网日期，不代表论文的发表时间）