Cart (Loading....) | Create Account
Close category search window

Improving item-based collaborative filtering recommendation system with tag

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

3 Author(s)
Song Hui ; Sch. of Manage., Harbin Inst. of Technol., Harbin, China ; Lu Pengyu ; Zhao Kai

Since market segmentation brings the need of personalized service and long tail phenomenon is continuously proven in the Internet applications, recommendation systems have been paid more and more attention. Item-based collaborative filtering algorithms as the one of most widely used and successful recommendation technology have been continuously improved. But traditional item-based collaborative filtering algorithms cannot solve the data sparseness and the "cold start" problems properly, and handle the over-reliance on the user rating information without consideration of the user's rating subjective factor. With the growing up of Web2.0, tag has been widely used, which allows users to define characteristics of objects from their own point of view. As a consequence, the interaction between the user and recommendation system is improved, and a new way of thinking to improve the quality of recommendation is provided as considering the view point of user in the recommendation. This paper uses tag-based method to calculate the similarity between users, and in the process of calculating item similarity, which makes use of TAG to calculate the similarity between the current user and each user in the candidate set to filter out users with different interest points, thereby it enhances the credibility of item similarity and guarantees the quality of recommendation quality as well. And based on mentioned above, the recommendation system framework is designed, meanwhile which facilitates further research.

Published in:

Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC), 2011 2nd International Conference on

Date of Conference:

8-10 Aug. 2011

Need Help?

IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.