Notice of Violation of IEEE Publication Principles - Hybrid Recommender Systems: Content-Boosted Collaborative Filtering for Improved Recommendations

Abstract

Notice of Violation of IEEE Publication Principles<br><br>"Hybrid Recommender Systems: Content-Boosted Collaborative Filtering for Improved Recommendations"<br>by Vipul Vekariya and G.R. Kulkarni<br>in Proceedings of the 2012 International Conference on Communication Systems and Network Technologies (CSNT), 2012, pp. 649-653<br><br>After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles.<br><br>This paper contains large portions of text from the papers cited below. The text and figures were copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.<br><br>"Hybrid Collaborative Filtering and Content-Based Filtering for Improved Recommender System"<br>by Kyung-Yong Jung, Dong-Hyun Park and Jung-Hyun Lee<br>in Lecture Notes in Computer Science, 2004, Volume 3036/2004, Springer, pp. 295-302<br><br>"A Hybrid Approach for Movie Recommendation"<br>by George Lekakos and Petros Caravelas<br>in Multimedia Tools and Applications, Volume 36, Numbers 1-2, January 2008, Springer, pp. 55-70<br><br>"Hybrid Recommender Systems: Survey and Experiments"<br>by Robin Burke<br>in User Modeling and User-Adapted Interaction, Volume 12 Issue 4, November 2002, pp. 331-370<br><br>"Content-Boosted Collaborative Filtering for Improved Recommendations"<br>by Prem Melville, Raymond J. Mooney, Ramadass Nagarajan<br>in the Proceedings of the 2002 American Association for Artificial Intelligence, pp. 187 - 192<br><br> <br/> Recommender systems represent user preferences for the purpose of suggesting items to purchase or examine. They have become fundamental applications in electronic commerce and information access, providing suggestions that effectively prune large information spaces so that users are directed toward those items that best meet their needs and preferences. A variety of techniques have been proposed for performing recommendation, including content-based, collaborative, knowledge-based and other techniques. To improve performance, these methods have sometimes been combined in hybrid recommenders. This paper explains the landscape of actual and possible hybrid recommenders, and introduces a novel hybrid, a system that combines content boosted recommendation and collaborative Filtering to recommend restaurants.