Earticle

초록

영어

These days people are overwhelmed by information on the Internet thus searching for useful information becomes burdensome, often failing to acquire some in a reasonable time. Recommender systems are indispensable to fulfill such user needs through many practical commercial sites. This study proposes a novel similarity measure for user-based collaborative filtering which is a most popular technique for recommender systems. Compared to existing similarity measures, the main advantages of the suggested measure are that it takes all the ratings given by users into account for computing similarity, thus relieving the inherent data sparsity problem and that it reflects the uncertainty or vagueness of user ratings through fuzzy logic. Performance of the proposed measure is examined by conducting extensive experiments. It is found that it demonstrates superiority over previous relevant measures in terms of major quality metrics.

목차

Abstract
1. Introduction
2. Related Studies
2.1 Data Sparsity
2.2 Context of User Ratings
3. Proposed Similarity Estimation
3.1 User Rating Behavior and its Fuzzification
3.2 Proposed Similarity Measure
4. Performance Evaluation
4.1 Design of Experiments
4.2 Performance Results
5. Conclusions
References

키워드

저자

• Soojung Lee [ Professor, Department of Computer Education, Gyeongin National University of Education, Korea ] Corresponding author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  The International Journal of Advanced Smart Convergence

• 간기

  계간

• pISSN

  2288-2847

• eISSN

  2288-2855

• 수록기간

  2012~2025

• 십진분류

  KDC 326 DDC 380