A Collaborative Filtering Algorithm of Selecting Neighbors Based on User Profiles and Target Item

摘要：

　　Without considering the difference in user profiles and user rated items, traditional User-Based collaborative filtering recommendation algorithm only considers the usersscore on the item when calculates the similarity between users.In order to get rid of disadvantages of traditional methods, this paper proposes a collaborative filtering algorithm of selecting neighbors based on user profiles and target item.Aiming at obtaining target users neighbors more suitable, this paper uses a weighting coefficient to adjust the final similarity which is influences by user profiles similarity and users rating similarity.In the case of users neighbor didnt rate the target item, the expanded neighbors are considered, finally predicting and recommending target items.The experimental results show that the algorithm improves the accuracy of similarity, and effectively alleviates the user rating data sparseness problem, while improving the accuracy of the prediction.

关键词： Collaborative filtering similarity user profiles expanded neighbors

作者: Yaqiong Guo Mengxing Huang Tao Lou

作者单位: College of Information Science&Technology Hainan University Haikou 570228, China

会议类型: 国际会议

会议名称: The 12th Web Information System and Application Conference第十二届全国Web信息系统及其应用学术会议(WISA2015)、全国第十次语义Web 与本体论学术研讨会(SWON2015)、全国第九次电子政务技术及应用学术研讨会(EGTA2015)

会议地点: 济南

会议语种:英文

页码: 9-14

在线出版日期: 2015-09-11（万方平台首次上网日期，不代表论文的发表时间）