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http://dx.doi.org/10.5391/JKIIS.2013.23.1.7

Personalized Expert-Based Recommendation  

Chung, Yeounoh (Information & Intelligence System Lab., Sungkyunkwan University)
Lee, Sungwoo (Information & Intelligence System Lab., Sungkyunkwan University)
Lee, Jee-Hyong (Information & Intelligence System Lab., Sungkyunkwan University)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.23, no.1, 2013 , pp. 7-11 More about this Journal
Abstract
Taking experts' knowledge to recommend items has shown some promising results in recommender system research. In order to improve the performance of the existing recommendation algorithms, previous researches on expert-based recommender systems have exploited the knowledge of a common expert group for all users. In this paper, we study a problem of identifying personalized experts within a user group, assuming each user needs different kinds and levels of expert help. To demonstrate this idea, we present a framework for using Support Vector Machine (SVM) to find varying expert groups for users; it is shown in an experiment that the proposed SVM approach can identify personalized experts, and that the person-alized expert-based collaborative filtering (CF) can yield better results than k-Nearest Neighbor (kNN) algorithm.
Keywords
Support Vector Machine (SVM); Expert-based Recommender System; k-Nearest Neighbor; Collaborative Filtering;
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