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  • P-ISSN1013-0799
  • E-ISSN2586-2073
  • KCI

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Vol.24 No.2

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A Study on Recommendation System Using Data Mining Techniques for Large-sized Music Contents

Journal of the Korean Society for Information Management / Journal of the Korean Society for Information Management, (P)1013-0799; (E)2586-2073
2007, v.24 no.2, pp.89-104
https://doi.org/10.3743/KOSIM.2007.24.2.089


& (2007). A Study on Recommendation System Using Data Mining Techniques for Large-sized Music Contents. Journal of the Korean Society for Information Management, 24(2), 89-104, https://doi.org/10.3743/KOSIM.2007.24.2.089

Abstract

This study attempts to give a personalized recommendation framework in large-sized music contents environment. Despite of many existing studies and commercial solutions for a recommendation service, large online shopping malls are still looking for a recommendation system that can serve personalized recommendation and handle large data in real-time.This research utilizes data mining technologies and new pattern matching algorithm. A clustering technique is used to get dynamic user segmentations using user preference to contents categories. Then a sequential pattern mining technique is used to extract contents access patterns in the user segmentations. Finally, the recommendation is given by our recommendation algorithm using user contents preference history and contents access patterns of the segment. In the framework, preprocessing and data transformation and transition are implemented on DBMS. The proposed system is implemented to show that the framework is feasible. In the experiment using real-world large data, personalized recommendation is given in almost real-time and shows acceptable correctness.

keywords
개인화, 추천시스템, 순차패턴, 군집화. 데이터마이닝, Personalization, Recommendation system, Sequential Patterns, Clustering, Data Mining

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