Earticle

초록

영어

The proliferation of items increased the difficulty of customers in finding the specific items they want to purchase. To solve this problem, companies adopted recommender systems, such as collaborative filtering systems, to provide personalization services. However, companies use only meaningful and essential data given the explosive growth of data. Some customers are concerned that their private information may be exposed because CF systems necessarily deal with personal information. Based on these concerns, we analyze the effects of the amount of information on recommendation performance. We assume that a customer could choose to provide overall information or partial information. Experimental results indicate that customers who provided overall information generally demonstrated high performance, but differences exist according to the characteristics of products. Our study can provide companies with insights concerning the efficient utilization of data.

목차

ABSTRACT
 Ⅰ. Introduction
 Ⅱ. Related Work
 Ⅲ. Methodology
  3.1. Overall Procedure
  3.2. Data Preprocessing and Profiling
  3.3. Sampling
  3.4. Similarity Calculation
  3.5. Selection of Top-N Items
  3.6. Comparison of the Recommendation Performance
 Ⅳ. Experiments
  4.1. Data Set
  4.2. Experimental Results
 Ⅴ. Conclusion
 Acknowledgement
 

키워드

저자

• Hyun Sil Moon [ Research Doctor, School of Management, Kyung Hee University, Korea ]
• Jung Hyun Yoon [ Researcher, School of Management, Kyung Hee University, Korea ]
• Il Young Choi [ Visiting Professor, School of Management, Kyung Hee University, Korea ]
• Jae Kyeong Kim [ Professor, School of Management, Kyung Hee University, Korea ] Corresponding author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  Asia Pacific Journal of Information Systems

• 간기

  계간

• pISSN

  2288-5404

• eISSN

  2288-6818

• 수록기간

  1990~2026

• 등재여부

  KCI 등재,SCOPUS

• 십진분류

  KDC 325 DDC 658