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Semantic Content-Based Recommendations Using Semantic Graphs

Conference paper
First Online:
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 680)

Abstract

Recommender systems (RSs) can be useful for suggesting items that might be of interest to specific users. Most existing content-based recommendation (CBR) systems are designed to recommend items based on text content, and the items in these systems are usually described with keywords. However, similarity evaluations based on keywords suffer from the ambiguity of natural languages. We present a semantic CBR method that uses Semantic Web technologies to recommend items that are more similar semantically with the items that the user prefers. We use semantic graphs to represent the items and we calculate the similarity scores for each pair of semantic graphs using an inverse graph frequency algorithm. The items having higher similarity scores to the items that are known to be preferred by the user are recommended.

Keywords

Information retrieval Ontology Semantic graph Content-based recommendation Semantic matching 
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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Science Integration Program (Human), Department of Frontier Sciences and Science Integration, Division of Project CoordinationThe University of TokyoKashiwaJapan

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