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http://dx.doi.org/10.5391/JKIIS.2005.15.7.887

A Real-time Service Recommendation System using Context Information in Pure P2P Environment  

Lee Se-Il (공주대학교 컴퓨터공학과)
Lee Sang-Yong (공주대학교 컴퓨터공학부)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.15, no.7, 2005 , pp. 887-892 More about this Journal
Abstract
Under pure P2P environments, collaborative filtering must be provided with only a few service items by real time information without accumulated data. However, in case of collaborative filtering with only a few service items collected locally, quality of recommended service becomes low. Therefore, it is necessary to research a method to improve quality of recommended service by users' context information. But because a great volume of users' context information can be recognized in a moment, there can be a scalability problem and there are limitations in supporting differentiated services according to fields and items. In this paper, we solved the scalability problem by clustering context information Per each service field and classifying il per each user, using SOM. In addition, we could recommend proper services for users by measuring the context information of the users belonging to the similar classification to the service requester among classified data and then using collaborative filtering.
Keywords
Collaborative Filtering; Contort; Pure P2P; SOM; Recommendation System;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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