바로가기메뉴

본문 바로가기 주메뉴 바로가기

logo

Article Contents

Prev Next
e-Submission

Vol.24 No.2
Citation Share

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
  • Downloaded
  • Viewed
PDF Download

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

Reference

1.

Balabanovic, M. (1997). Fab: Content-based, collaborative recommendation. 40(3), 66-72.

2.

Basu, C. (1998). Recommendation as Classification Using and Content-based Information in Recommendation. , 714-720.

3.

Claypool, M. (1999). Combining content-based and collaborative filters in an online newspaper. , -.

4.

Hill, W. (1995). Recommending and evaluating choices in a virtual community of use. , 194201-.

5.

Choi, Hyun-Wha. (2005). Multi-Level Linear Location Tree for Efficient Sequential Pattern Mining. 277(1), 369-374.

6.

Kim, Hyun-Hee. (2002). A Study on the Design and Evaluation of the Model of MyCyber Library for a Customized Information Service. 19(2), 132-157.

7.

Kim, Yong. (2005). A Study on Development of Hybrid Personalization Recommendation System based on Learning Algorithm. 39(3), 75-81.

8.

Mobasher, B. H. (2000). Discovery of aggregate usage profiles for web personalization. , -.

9.

N. Iacovou. An open architecture for collaborative filtering of NetNews Proc. of the ACM CSCW’94 Conference on Computer-Supported Cooperative Work. , -.

10.

Sarwar, B. (2000). Analysis of recommendation algorithms for e-commerce. , -.

11.

Sarwar, B. (2001). Item-based collaborative filtering recommendation algorithms. , -.

12.

Shardanand, U. (1995). Social information filtering: Algorithms for word of mouth. , 210-217.

13.

Srikant, Ramakrishnan. (1998). Mining Sequential Patterns: Generalizations and Performance Improvements. , -.

14.

Weng, Sung-Shun. (2004). Feature-based recommendations for one-to-one marketing. 26(4), 493-508.

Journal of the Korean Society for Information Management

e-Submission OA Policy