Collaborative Filtering Recommendation Algorithm Based on Improved Similarity Computing

被引：0
|
作者：
Liu, Aili [1 ]
Li, Baoan [1 ]
机构：
[1] Beijing Informat Sci & Technol Univ, Comp Sch, Beijing, Peoples R China
来源：
PROCEEDINGS OF THE 4TH INTERNATIONAL CONFERENCE ON MECHATRONICS, MATERIALS, CHEMISTRY AND COMPUTER ENGINEERING 2015 (ICMMCCE 2015) | 2015年 / 39卷
关键词：
Personalized Recommendation; Collaborative Filtering; MAE (Mean Absolute Error); User Characteristic; Item Attribute; SYSTEMS;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
At presently the most widely used algorithm is collaborative filtering in the Personalized Recommendation Systems for E-Commerce. Aiming at the problem that the recommendation is not accurate due to the data sparsity, a collaborative filtering algorithm based on user characteristics and item attributes preference was proposed in this study. It obtained the nearest neighbor users and similar items by analyzing the user characteristics, item attributes and the data of user's historical scores, and then computing the similarity between the two users based on the user-based collaborative filtering algorithm. It gave an algorithm which has the lower value of MAE and may improve the accuracy of the recommendation services.
引用
收藏
页码：1375 / 1379
页数：5
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