Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Hybrid Recommender Systems: Survey and Experiments

Hybrid Recommender Systems: Survey and Experiments 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 surveys the landscape of actual and possible hybrid recommenders, and introduces a novel hybrid, EntreeC, a system that combines knowledge-based recommendation and collaborative filtering to recommend restaurants. Further, we show that semantic ratings obtained from the knowledge-based part of the system enhance the effectiveness of collaborative filtering. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png User Modeling and User-Adapted Interaction Springer Journals

Hybrid Recommender Systems: Survey and Experiments

User Modeling and User-Adapted Interaction , Volume 12 (4) – Oct 18, 2004

Loading next page...
 
/lp/springer-journals/hybrid-recommender-systems-survey-and-experiments-6dmewZRSZc

References (85)

Publisher
Springer Journals
Copyright
Copyright © 2002 by Kluwer Academic Publishers
Subject
Computer Science; User Interfaces and Human Computer Interaction; Multimedia Information Systems; Management of Computing and Information Systems
ISSN
0924-1868
eISSN
1573-1391
DOI
10.1023/A:1021240730564
Publisher site
See Article on Publisher Site

Abstract

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 surveys the landscape of actual and possible hybrid recommenders, and introduces a novel hybrid, EntreeC, a system that combines knowledge-based recommendation and collaborative filtering to recommend restaurants. Further, we show that semantic ratings obtained from the knowledge-based part of the system enhance the effectiveness of collaborative filtering.

Journal

User Modeling and User-Adapted InteractionSpringer Journals

Published: Oct 18, 2004

There are no references for this article.