Collaborative Filtering based on User Attributes and User Ratings for Restaurant Recommendation

摘要：

　　Online recommendation service had brought economic benefits for traditional catering industry.Aimed at the status quo,user-based collaborative filtering(UCF)algorithm was applied to restaurant recommendations in this paper.However,users preference about restaurant was affected by many factors,leading traditional UCF algorithm precision was low.In order to solve this problem,three improvement were proposed.Firstly,mean score was enhanced to the calculation of similarity.Secondly,the number of common items between two users was utilized to affect the credibility of similarity,so modification factor was added to weaken the pseudo similar error.Finally,the real personal information online users registered were used to calculate the similarity based on users attributes.The experimental results show that the modified algorithm(AdvancedCF)can improve the accuracy of the similarity calculation and provide users with more accurate restaurant recommendations.

关键词： collaborative filtering restaurant recommendation score prediction user attributes user similarity

作者: Ling Li Ya Zhou Han Xiong Cailin Hu Xiafei Wei

作者单位: School of Computer and Information Security,Guilin University of Electronic Technology Guilin Guangxi,China

会议类型: 国际会议

会议名称: 2017 IEEE 2nd Advanced Information Technology,Electronic and Automation Control Conference(IAEAC 2017)(2017 IEEE 第2届先进信息技术、电子与自动化控制国际会议)

会议地点: 重庆

会议语种:英文

页码: 2592-2597

在线出版日期: 2017-03-25（万方平台首次上网日期，不代表论文的发表时间）