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A Deep Learning Based Recommender System Using Visual Information

시각 정보를 활용한 딥러닝 기반 추천 시스템

  • Moon, Hyunsil (Graduate School of Business Administration, Kookmin University) ;
  • Lim, Jinhyuk (School of Business Administration, Kookmin University) ;
  • Kim, Doyeon (School of Business Administration, Kookmin University) ;
  • Cho, Yoonho (School of Business Administration, Kookmin University)
  • 문현실 (국민대학교 경영대학원) ;
  • 임진혁 (국민대학교 경영학부 빅데이터경영통계전공) ;
  • 김도연 (국민대학교 경영학부 빅데이터경영통계전공) ;
  • 조윤호 (국민대학교 빅데이터경영통계전공)
  • Received : 2020.07.04
  • Accepted : 2020.08.08
  • Published : 2020.09.30

Abstract

In order to solve the user's information overload problem, recommender systems infer users' preferences and suggest items that match them. The collaborative filtering (CF), the most successful recommendation algorithm, has been improving performance until recently and applied to various business domains. Visual information, such as book covers, could influence consumers' purchase decision making. However, CF-based recommender systems have rarely considered for visual information. In this study, we propose VizNCS, a CF-based deep learning model that uses visual information as additional information. VizNCS consists of two phases. In the first phase, we build convolutional neural networks (CNN) to extract visual features from image data. In the second phase, we supply the visual features to the NCF model that is known to easy to extend to other information among the deep learning-based recommendation systems. As the results of the performance comparison experiments, VizNCS showed higher performance than the vanilla NCF. We also conducted an additional experiment to see if the visual information affects differently depending on the product category. The result enables us to identify which categories were affected and which were not. We expect VizNCS to improve the recommender system performance and expand the recommender system's data source to visual information.

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