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

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 semanti...

Full description

Saved in:
Bibliographic Details
Published in2011 International Conference on Information Science and Applications pp. 1 - 8
Main Authors Jinbeom Kang, Joongmin Choi
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2011
Subjects
Mathematical model
Monitoring
Ontologies
Robots
Semantics
Sports equipment
Training
Online AccessGet full text

Cover

More Information
Summary: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.
ISBN:9781424492220
142449222X
ISSN:2162-9048
DOI:10.1109/ICISA.2011.5772322