Earticle

초록

영어

The tremendous growth of data in recent years poses some key challenges for recommender systems. Theses keys are related with producing high quality recommendations and fast performing the composition recommended items. In this paper, we propose social clustering-based similar user index to not only improve the prediction of recommendations, but also compose personalized recommendations in fast. Through the experimental result, we show that proposed clustering method is more accurate than k-means which is prevalent clustering techniques. And, we reduce computation time needed for composing recommendation. That is, proposed clustering-based indexing method improves the performance of recommender systems which deals with a very large data.

목차

Abstract
 1. Introduction
 2. Related Works and Backgrounds
  2.1. Collaborative Filtering
  2.2. Clustering
  2.3. Recommender Algorithms with the Social Network
 3. Social Clustering-based Similar User Indexing Mechanism
  3.1. Generating Social Network with the Recommender Dataset
  3.2. Three Steps-clustering for Composing the Similar User Index
  3.3. Searching Similar Users with the Similar User Index
 4. Evaluation
  4.1. Response Time
  4.2. Clustering Accuracy
 5. Conclusion
 Acknowledgement
 References

키워드

저자

• Haesung Lee [ Department of Computer Science, Kyonggi University, San 94-6, Yiui-dong, Yeongtong-ku, Suwon-si, Gyeonggi-do, Korea ]
• Joonhee Kwon [ Department of Computer Science, Kyonggi University, San 94-6, Yiui-dong, Yeongtong-ku, Suwon-si, Gyeonggi-do, Korea ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605