Welcome to the InfoSci Platform
Reference Hub1
Utilizing Association Rules for Improving the Performance of Collaborative Filtering

Utilizing Association Rules for Improving the Performance of Collaborative Filtering

Zainab Khanzadeh, Mehregan Mahdavi
Copyright: © 2012 |Volume: 3 |Issue: 2 |Pages: 15
ISSN: 1947-8585|EISSN: 1947-8593|EISBN13: 9781466611887|DOI: 10.4018/jeei.2012040102
Cite Article Cite Article

MLA

Khanzadeh, Zainab, and Mehregan Mahdavi. "Utilizing Association Rules for Improving the Performance of Collaborative Filtering." IJEEI vol.3, no.2 2012: pp.14-28. http://doi.org/10.4018/jeei.2012040102

APA

Khanzadeh, Z. & Mahdavi, M. (2012). Utilizing Association Rules for Improving the Performance of Collaborative Filtering. International Journal of E-Entrepreneurship and Innovation (IJEEI), 3(2), 14-28. http://doi.org/10.4018/jeei.2012040102

Chicago

Khanzadeh, Zainab, and Mehregan Mahdavi. "Utilizing Association Rules for Improving the Performance of Collaborative Filtering," International Journal of E-Entrepreneurship and Innovation (IJEEI) 3, no.2: 14-28. http://doi.org/10.4018/jeei.2012040102

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

Internet technology has rapidly grown during the last decades. Presently, users are faced with a great amount of information and they need help to find appropriate items in the shortest possible time. Recommender systems were introduced to overcome this problem of overloaded information. They recommend items of interest to users based on their expressed preferences. Major e-commerce companies try to use this technology to increase their sales. Collaborative Filtering is the most promising technique in recommender systems. It provides personalized recommendations according to user preferences. But one of the problems of Collaborative Filtering is cold-start. The authors provide a novel approach for solving this problem through using the attributes of items in order to recommend items to more people for improving e-business activities. The experimental results show that the proposed method performs better than existing methods in terms of the number of generated recommendations and their quality.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.