Building Recommender Strategies Ontology for Intelligent Recommender System

Nowadays, with the rapid development of Web2. 0, adding, sharing and rating information is much easier than before. Confronting with the tremendous varieties of available information sources, web users find it increasingly difficult to obtain valuable information effectively. Recommender systems can help users to quickly find the information they need. A variety of techniques have been proposed for performing recommendation, including content - based, collaborative filtering, knowledge - based, utility - based and other techniques. However, every recommender system uses only one single recommendation technique. To avoid insufficiency of any single recommendation technique and adapt specific recommendation techniques to particular situations that the web users face, in this paper, different recommendation techniques and users' behavior are analyzed, and the Recommender Strategies Ontology for intelligent recommender systems is proposed.