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다국어 초록 (Multilingual Abstract)

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.

참고문헌 (Reference)

1 조윤호, "재구성된 제품 계층도를 이용한 협업 추천 방법론 및 그 평가" 한국경영과학회 29 (29): 59-75, 2004

2 이재식, "장르별 협업필터링을 이용한 영화추천시스템의 성능 향상" 한국지능정보시스템학회 13 (13): 65-78, 2007

3 정석봉, "인터넷 종합쇼핑몰에서 상품네트워크의 구조적 특성과 매출성과 간의 상관관계 분석" 한국경영교육학회 30 (30): 433-453, 2015

4 박종학, "사회연결망：신규고객 추천문제의 새로운 접근법" 한국지능정보시스템학회 15 (15): 123-140, 2009

5 김민정, "빅데이터 기반 추천시스템 구현을 위한 다중 프로파일 앙상블 기법" 한국지능정보시스템학회 21 (21): 93-110, 2015

6 김경재, "개인화된 추천시스템을 위한 사용자-상품 매트릭스 축약기법" 한국데이타베이스학회 16 (16): 97-113, 2009

7 임치환, "감성공학을 이용한 온라인 추천 서비스 알고리즘" 한국산업경영시스템학회 27 (27): 38-46, 2004

8 김철진, "XPDL 기반 모바일웹 추천기법" 한국산학기술학회 14 (14): 5856-5865, 2013

9 Niwattanakul, S., "Using of Jaccard Coefficient for Keywords Similarity" 1 : 13-15, 2013

10 Ali, K., "Tivo : Making show recommendations using a distributed collaborative filtering architecture" 394-401, 2004