By Topic

An Item Based Collaborative Filtering Recommendation Algorithm Using Rough Set Prediction

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Ping Su ; Zhejiang Bus. Technol. Inst., Ningbo, China ; HongWu Ye

Recommender systems represent personalized services that aim at predicting userspsila interest on information items available in the application domain. Collaborative filtering technique has been proved to be one of the most successful techniques in recommendation systems in recent years. Poor quality is one major challenge in collaborative filtering recommender systems. Sparsity of userspsila ratings is the major reason causing the poor quality. To solve this problem, this paper proposed an item based collaborative filtering recommendation algorithm using the rough set theory prediction. This method employs rough set theory to fill the vacant ratings of the user-item matrix where necessary. Then it utilizes the item based collaborative filtering to produce the recommendation. The experiments were made on a common data set using different filtering algorithms. The results show that the proposed recommender algorithm combining rough set theory and item based collaborative filtering can improve the accuracy of the collaborative filtering recommendation system.

Published in:

Artificial Intelligence, 2009. JCAI '09. International Joint Conference on

Date of Conference:

25-26 April 2009