A semantic social network-based expert recommender system
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A semantic social network-based expert recommender system Elnaz Davoodi · Keivan Kianmehr · Mohsen Afsharchi
Published online: 12 October 2012 © Springer Science+Business Media, LLC 2012
Abstract This research work presents a framework to build a hybrid expert recommendation system that integrates the characteristics of content-based recommendation algorithms into a social network-based collaborative filtering system. The proposed method aims at improving the accuracy of recommendation prediction by considering the social aspect of experts’ behaviors. For this purpose, content-based profiles of experts are first constructed by crawling online resources. A semantic kernel is built by using the background knowledge derived from Wikipedia repository. The semantic kernel is employed to enrich the experts’ profiles. Experts’ social communities are detected by applying the social network analysis and using factors such as experience, background, knowledge level, and personal preferences. By this way, hidden social relationships can be discovered among individuals. Identifying communities is used for determining a particular member’s value according to the general pattern behavior of the community that the individual belongs to. Representative members of a community are then identified using the eigenvector centrality measure. Finally, a recommendation is made to relate an information item, for which a user is seeking an expert, to the representa-
E. Davoodi Department of Computer Science, Institute for Advanced Studies in Basic Sciences, Zanjan, Iran e-mail: [email protected] K. Kianmehr () Department of Electrical and Computer Engineering, Western University, London, Canada e-mail: [email protected] M. Afsharchi Department of Electrical and Computer Engineering, University of Zanjan, Zanjan, Iran e-mail: [email protected]
tives of the most relevant community. Such a semantic social network-based expert recommendation system can provide benefits to both experts and users if one looks at the recommendation from two perspectives. From the user’s perspective, she/he is provided with a group of experts who can help the user with her/his information needs. From the expert’s perspective she/he has been assigned to work on relevant information items that fall under her/his expertise and interests. Keywords Semantic information extraction · Social network analysis · Expert recommender system · Knowledge management
1 Introduction In the past decade, lots of major internet retailers have begun to build recommender systems to personalize content to show to their users through an information filtering process. The principal objective of any recommendation system is that of complexity reduction for the human being, sifting through very large sets of information and selecting those pieces that are relevant to the active user’s needs. Thus, all of significant recommender systems create a profile for each user and then use one of recommendation approaches to make recommendations about information items a user might be interested i
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