DocumentCode :
588782
Title :
Collaborative Filtering with Improved Item Prediction Approach for Enhancing the Accuracy of Recommendation
Author :
Duan Long-Zhen ; Wang Gui-Fen ; Ren Yan
Author_Institution :
Dept. of Comput. Applic. Technol., Nanchang Univ., Nanchang, China
fYear :
2012
fDate :
2-4 Nov. 2012
Firstpage :
349
Lastpage :
352
Abstract :
Collaborative filtering (CF) is a widely-used technique for generating personalized recommendations. CF systems are typically based on the ratings given by users to items. There are many factors influencing user´s rating, beside user´s interest and rating scale, item objective character is also the important element. Considering these factors, the improved item prediction approaches present a more rational method to measure user´s rating scale, take item objective character into consideration in the processing of prediction. CF with improved prediction approaches are empirically tested in recommendation and shown better recommendation accuracy than traditional CF.
Keywords :
collaborative filtering; recommender systems; CF systems; collaborative filtering; improved item prediction approaches; item objective character; personalized recommendation accuracy; user interest; user rating scale; Accuracy; Algorithm design and analysis; Collaboration; Correlation; Filtering; Measurement uncertainty; Prediction algorithms; accuracy; collaborative filtering; item objective character; item prediction approach;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Multimedia Information Networking and Security (MINES), 2012 Fourth International Conference on
Conference_Location :
Nanjing
Print_ISBN :
978-1-4673-3093-0
Type :
conf
DOI :
10.1109/MINES.2012.87
Filename :
6405695
Link To Document :
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