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-Spear: A New Method for Expert Based Recommendation Systems

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Recommendation systems are based on a fast and effective personalized mechanism to provide items relevant to users. In this article, an expert-based approach for recommendation is proposed. We extend the spamming-resistant expertise analysis and ranking (SPEAR) algorithm to determine a set of experts from a set of attributes and values, calling the modification the -SPEAR algorithm. This system can recommend a set of items to users using expert opinions. In this approach, we use ontology to build profiles of users. The experimental results are implemented in the movie domain as a case study. Our data set was collected from IMDB and MovieLens data sets.
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Keywords: attribute value; item profile; ontology; recommendation systems; user profile

Document Type: Research Article

Affiliations: 1: Faculty of Mathematics and Informatics, Quang Binh University, Dong Hoi, Vietnam 2: Yeungnam University, Gyeongsan, Korea 3: Wroclaw University of Technology, Wroclaw, Poland

Publication date: February 17, 2014

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