Pet Shop Recommendation System based on Implicit Feedback

• Published in: 디지털콘텐츠학회논문지 Vol. 18; no. 8; pp. 1561 - 1566
• Main Authors: Heeyoul Choi(최희열), Yunhee Kang(강윤희), Myungju Kang(강명주)
• Format: Journal Article
• Language: English
• Published: 한국디지털콘텐츠학회 01.12.2017
• Subjects: 컴퓨터학; 암묵적 피드백; Click information on items; 추천 시스템; Implicit feedback; Machine learning; Recommendation systems; 기계 학습; 상품 클릭 정보
• ISSN: 1598-2009; 2287-738X
• DOI: 10.9728/dcs.2017.18.8.1561

Due to the advances in machine learning and artificial intelligence technologies, many new services have become available. Among such services, recommendation systems have already been successfully applied to commercial services and made profits as in online shopping malls. Most recommendation algorithms in commercial services are based on content analysis or explicit feedback rates as in movie recommendations. However, many online shopping malls have difficulties in content analysis or are lacking explicit feedbacks on their items, which results in no recommendation system for their items. Even for such service systems, user log data is easily available, and if recommendations are possible with such log data, the quality of their service can be improved. In this paper, we extract implicit feedback like click information for items from log data and provide a recommendation system based on the implicit feedback. The proposed system is applied to a real in-service online shopping mall. KCI Citation Count: 6