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An optimized item-based collaborative filtering recommendation algorithm

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4 Author(s)
Jinbo Zhang ; Pattern Recognition & Intelligent System Lab, Beijing University of Posts and Telecommunications, China ; Zhiqing Lin ; Bo Xiao ; Chuang Zhang

Collaborative filtering is a very important technology in e-commerce. Unfortunately, with the increase of users and commodities, the user rating data is extremely sparse, which leads to the low efficient collaborative filtering recommendation system. To address these issues, an optimized collaborative filtering recommendation algorithm based on item is proposed. While calculating the similarity of two items, we obtain the ratio of users who rated both items to those who rated each of them. The ratio is taken into account in this method. The experimental results show that the proposed algorithm can improve the quality of collaborative filtering.

Published in:

2009 IEEE International Conference on Network Infrastructure and Digital Content

Date of Conference:

6-8 Nov. 2009