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

Dynamic Item Recommendation by Topic Modeling for Social Networks

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)
Sang Su Lee ; Comput. Sci. Dept., Univ. of Southern California, Los Angeles, CA, USA ; Tagyoung Chung ; McLeod, D.

The need to identify an approach that recommends items that match users' preferences within social networks has grown in tandem with the increasing number of items appearing within these networks. This research presents a novel technique for item recommendation within social networks that matches user and group interests over time. Users often tag items in social networks with words and phrases that reflect their preferred "vocabulary." As such, these tags provide succinct descriptions of the resource, implicitly reveal user preferences, and, as the tag vocabulary of users tends to change over time, reflect the dynamics of user preferences. Based on evaluation of user and group interests over time, we present a recommendation system employing a modified latent Dirichlet allocation (LDA) model in which users and tags associated with an item are represented and clustered by topics, and the topic-based representation is combined with the item's timestamp to show time-based topic distribution. By representing users via topics, the model can cluster users to reveal the group interests. Based on this model, we developed a recommendation system that reflects user as well as group interests in a dynamic manner that accounts for time, allowing it to perform in a manner superior to that of static recommendation systems in terms of precision rate.

Published in:

Information Technology: New Generations (ITNG), 2011 Eighth International Conference on

Date of Conference:

11-13 April 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.