An Ontology-Based Recommendation System Using Long-Term and Short-Term Preferences

Abstract

Personalized information retrieval and recommendation systems have been proposed to deliver the right information to users with different interests. However, most of previous systems are using keyword frequencies as the main factor for personalization, and as a result, they could not analyze semantic relations between words. Also, previous methods often fail to provide the documents that are related semantically with the query words. To solve these problems, we propose a recommendation system which provides relevant documents to users by identifying semantic relations between an ontology that semantically represents the documents crawled by a Web robot and user behavior history. Recommendation is mainly based on content-based similarity, semantic similarity, and preference weights.