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신용카드 추천을 위한 다중 프로파일 기반 협업필터링 KCI 등재

Collaborative Filtering for Credit Card Recommendation based on Multiple User Profiles

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  • URLhttps://db.koreascholar.com/Article/Detail/338625
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한국산업경영시스템학회지 (Journal of Society of Korea Industrial and Systems Engineering)
한국산업경영시스템학회 (Society of Korea Industrial and Systems Engineering)
초록

Collaborative filtering, one of the most widely used techniques to build recommender systems, is based on the idea that users with similar preferences can help one another find useful items. Credit card user behavior analytics show that most customers hold three or less credit cards without duplicates. This behavior is one of the most influential factors to data sparsity. The ‘cold-start’ problem caused by data sparsity prevents recommender system from providing recommendation properly in the personalized credit card recommendation scenario. We propose a personalized credit card recommender system to address the cold-start problem, using multiple user profiles. The proposed system consists of a training process and an application process using five user profiles. In the training process, the five user profiles are transformed to five user networks based on the cosine similarity, and an integrated user network is derived by weighted sum of each user network. The application process selects k-nearest neighbors (users) from the integrated user network derived in the training process, and recommends three of the most frequently used credit card by the k-nearest neighbors. In order to demonstrate the performance of the proposed system, we conducted experiments with real credit card user data and calculated the F1 Values. The F1 value of the proposed system was compared with that of the existing recommendation techniques. The results show that the proposed system provides better recommendation than the existing techniques. This paper not only contributes to solving the cold start problem that may occur in the personalized credit card recommendation scenario, but also is expected for financial companies to improve customer satisfactions and increase corporate profits by providing recommendation properly.

목차
1. 서 론
 2. 고객맞춤형 신용카드 추천 기법
  2.1 학습과정 : 통합 고객 네트워크 IUN 구성
  2.2 적용과정 : IUN을 이용한 추천 카드 도출
 3. 실 험
  3.1 실험 데이터
  3.2 분석결과
 4. 결 론
 Acknowledge
 References
저자
  • 이원철(경일대학교 경영학과) | Won Cheol Lee (School of Business, Kyungil University)
  • 윤협상(대구가톨릭대학교 경영학부) | Hyoup Sang Yoon (Business School, Daegu Catholic University)
  • 정석봉(경일대학교 철도학과) | Seok Bong Jeong (Dep. of Railway, Kyungil University) Corresponding author