DocumentCode :
3773543
Title :
Collaborative Filtering Recommendation Algorithm Optimization Based on User Attributes
Author :
Yu Zeng;Yuan Bi;Jie Wang;Yun Lin
Author_Institution :
Sch. of Econ., Peking Univ., Beijing, China
Volume :
1
fYear :
2015
Firstpage :
580
Lastpage :
583
Abstract :
Aiming at the data sparse and cold start problems in collaborative filtering recommendation algorithm, an optimized solution based on user characteristics and user ratings is proposed in this paper. Based on users´ basic attributes and users´ history score record, the similarity of users and the similarity of items are calculated, and the nearest neighbor users and similar items are obtained. The advantage of the algorithm is that it combines the user´s score and personal attributes to calculate the similarity between users and to recommend items. The optimized algorithm is applied to the recommendation of insurance products. Experiments based on real data from insurance company show that this method can reduce the average absolute error and improve the accuracy of recommendation.
Keywords :
"Collaboration","Insurance","Filtering","Filtering algorithms","Urban areas","Prediction algorithms","Algorithm design and analysis"
Publisher :
ieee
Conference_Titel :
Computational Intelligence and Design (ISCID), 2015 8th International Symposium on
Print_ISBN :
978-1-4673-9586-1
Type :
conf
DOI :
10.1109/ISCID.2015.91
Filename :
7469021
Link To Document :
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