Earticle

초록

영어

In this paper, we evaluate performance of existing similarity measurement metric and propose a novel method using user’s preferences information entropy to reduce MAE in memory-based collaborative recommender systems. The proposed method applies a similarity of individual inclination to traditional similarity measurement methods. We experiment on various similarity metrics under different conditions, which include an amount of data and significance weighting from n/10 to n/60, to verify the proposed method. As a result, we confirm the proposed method is robust and efficient from the viewpoint of a sparse data set, applying existing various similarity measurement methods and Significance Weighting.

목차

Abstract
 1. Introduction
 2. Proposed Method Using User Preference Information Entropy
 3. Experiments and Results
 4. Conclusion
 References

키워드

저자

• Hyeong-Joon Kwon [ School of Information and Communication Engineering, Sungkyunkwan University, South Korea ] Corresponding Author

참고문헌

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간행물

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• 간행물명

  International Journal of Advanced Culture Technology(IJACT)

• 간기

  계간

• pISSN

  2288-7202

• eISSN

  2288-7318

• 수록기간

  2013~2025

• 등재여부

  KCI 등재

• 십진분류

  KDC 600 DDC 700