Hybrid Recommender Systems: Survey and Experiments Export

@article{citeulike:304193, 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.}, address = {Hingham, MA, USA}, author = {Burke, Robin}, booktitle = {User Modeling and User-Adapted Interaction}, citeulike-article-id = {304193}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=586352}, citeulike-linkout-1 = {http://dx.doi.org/10.1023/A:1021240730564}, citeulike-linkout-2 = {http://www.metapress.com/content/N881136032U8K111}, citeulike-linkout-3 = {http://www.springerlink.com/content/n881136032u8k111}, day = {1}, doi = {10.1023/A:1021240730564}, issn = {09241868}, journal = {User Modeling and User-Adapted Interaction}, keywords = {recommendation\_systems}, month = {November}, number = {4}, pages = {331--370}, posted-at = {2009-06-23 10:10:28}, priority = {2}, publisher = {Kluwer Academic Publishers}, title = {Hybrid Recommender Systems: Survey and Experiments}, url = {http://dx.doi.org/10.1023/A:1021240730564}, volume = {12}, year = {2002} }

TY - JOUR ID - citeulike:304193 L3 - citeulike-article-id:304193 N2 - 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. IS - 4 JF - User Modeling and User-Adapted Interaction SN - 09241868 AD - Hingham, MA, USA EP - 370 TI - Hybrid Recommender Systems: Survey and Experiments PB - Kluwer Academic Publishers VL - 12 T2 - User Modeling and User-Adapted Interaction SP - 331 KW - recommendation_systems AU - Burke, Robin PY - 2002/11/01/ UR - http://dx.doi.org/10.1023/A:1021240730564 ER -