Personalized Recommendation Considering Item Confidence in E-Commerce

온라인 쇼핑몰에서 상품 신뢰도를 고려한 개인화 추천

  • 최도진 (충북대학교 정보통신공학과) ;
  • 박재열 (충북대학교 정보통신공학과) ;
  • 박수빈 (충북대학교 빅데이터협동과정) ;
  • 임종태 (충북대학교 정보통신공학과) ;
  • 송재오 ((주) 제오시스 기업부설연구소) ;
  • 복경수 (충북대학교 정보통신공학과) ;
  • 유재수 (충북대학교 정보통신공학과)
  • Received : 2019.01.11
  • Accepted : 2019.02.18
  • Published : 2019.03.28


As online shopping malls continue to grow in popularity, various chances of consumption are provided to customers. Customers decide the purchase by exploiting information provided by shopping malls such as the reviews of actual purchasing users, the detailed information of items, and so on. It is required to provide objective and reliable information because customers have to decide on their own whether the massive information is credible. In this paper, we propose a personalized recommendation method considering an item confidence to recommend reliable items. The proposed method determines user preferences based on various behaviors for personalized recommendation. We also propose an user preference measurement that considers time weights to apply the latest propensity to consume. Finally, we predict the preference score of items that have not been used or purchased before, and we recommend items that have highest scores in terms of both the predicted preference score and the item confidence score.

CCTHCV_2019_v19n3_171_f0001.png 이미지

그림 1. 제안하는 기법의 구조도

CCTHCV_2019_v19n3_171_f0002.png 이미지

그림 2. 상품 별 선호도 계산 절차

CCTHCV_2019_v19n3_171_f0003.png 이미지

그림 3. 상품 신뢰도 판별 과정

CCTHCV_2019_v19n3_171_f0004.png 이미지

그림 4. 상품 추천 과정

CCTHCV_2019_v19n3_171_f0005.png 이미지

그림 5. 유사도 기반 상품 예측 점수 계산

CCTHCV_2019_v19n3_171_f0006.png 이미지

그림 6. 추천 기법 및 사용자에 따른 CTR

CCTHCV_2019_v19n3_171_f0007.png 이미지

그림 7. 추천 기법에 따른 평균 CTR

표 1. 상품에 대한 행위 저장 테이블

CCTHCV_2019_v19n3_171_t0001.png 이미지

표 2. 행위 값 정규화 테이블

CCTHCV_2019_v19n3_171_t0002.png 이미지

표 3. 행위 비율 테이블

CCTHCV_2019_v19n3_171_t0003.png 이미지

표 4. 행위 가중치 테이블

CCTHCV_2019_v19n3_171_t0004.png 이미지

표 5. 성향을 고려한 상품 선호도 테이블

CCTHCV_2019_v19n3_171_t0005.png 이미지

표 6. 성능 평가 환경

CCTHCV_2019_v19n3_171_t0006.png 이미지


Supported by : 한국연구재단


  1. R. A. Lewis and D. H. Reiley, "Online ads and offline sales: measuring the effect of retail advertising via a controlled experiment on Yahoo!," Quantitative Marketing and Economics, Vol.12, No.3, pp.235-266, 2014.
  2. I. M. Dinner, H. J. Van Heerde, and S. A. Neslin, "Driving online and offline sales: The cross-channel effects of traditional, online display, and paid search advertising," Journal of Marketing Research, Vol.51, No.5, pp.527-545, 2014.
  3. B. J. Corbitt, T. Thanasankit, and H. Yi, "Trust and e-commerce: a study of consumer perceptions," Electronic commerce research and applications, Vol.2, No.3, pp.203-215, 2003.
  4. A. Parasuraman, V. A. Zeithaml, and L. L. Berry, "Servqual: A multiple-item scale for measuring consumer perc," Journal of retailing, Vol.64, No.1, pp.12-31, 1988.
  5. B. Palese and A. Usai, "The relative importance of service quality dimensions in e-commerce experiences," International Journal of Information Management, Vol.40, pp.132-140, 2018.
  6. R. Abdelkhalek, I. Boukhris, and Z. Elouedi, "Assessing items reliability for collaborative filtering within the belief function framework," Proc. International Conference on Digital Economy, pp.208-217, 2017.
  7. S. Huang, Y. Chen, H. Chen, L. Chen, and Y. Fan, "Personalized item-of-interest recommendation on storage constrained smartphone based on word embedding quantization," Proc. Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp.610-621, 2018.
  8. Y. Jiang, J. Liu, M. Tang, and X. Liu, "An effective web service recommendation method based on personalized collaborative filtering," Proc. International Conference on In Web Services, pp.211-218, 2011.
  9. T. Ha and S. Lee, "Item-network-based collaborative filtering: A personalized recommendation method based on a user's item network," Information Processing and Management, Vol.53, No.5, pp.1171-1184, 2017.
  10. T. Ebesu and Y. Fang, "Neural Semantic Personalized Ranking for item cold-start recommendation," Information Retrieval Journal, Vol.20, No.2, pp.109-131, 2017.
  11., 2018. 2. 7.
  12., 2018. 2. 7.
  13. 이문철, 양정원, "리테일 마케팅 4.0: 더 오래 더 많이 팔리는 마케팅 실전 가이드," 21세기 북스, 2017.
  14. R. Baeza-Yates and B. Ribeiro-Neto, Modern information retrieval, ACM press, 1999.
  15., 2018. 2. 7.
  16., 2018. 2. 7.
  17., 2018. 2. 7.
  18. J. Beel, M. Genzmehr, S. Langer, A. Nurnberger, and B. Gipp, "A comparative analysis of offline and online evaluations and discussion of research paper recommender system evaluation," Proc. International workshop on reproducibility and replication in recommender systems evaluation, pp.7-14, 2013.