Journal of Information Technology Applications and Management

Korea Data Strategy Society (한국데이터전략학회)
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Deep Neural Network-Based Beauty Product Recommender

심층신경망 기반의 뷰티제품 추천시스템

  • Song, Hee Seok (Department of Global IT Business in Hannam University)
  • Received : 2019.09.20
  • Accepted : 2019.12.20
  • Published : 2019.12.31
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Abstract

Many researchers have been focused on designing beauty product recommendation system for a long time because of increased need of customers for personalized and customized recommendation in beauty product domain. In addition, as the application of the deep neural network technique becomes active recently, various collaborative filtering techniques based on the deep neural network have been introduced. In this context, this study proposes a deep neural network model suitable for beauty product recommendation by applying Neural Collaborative Filtering and Generalized Matrix Factorization (NCF + GMF) to beauty product recommendation. This study also provides an implementation of web API system to commercialize the proposed recommendation model. The overall performance of the NCF + GMF model was the best when the beauty product recommendation problem was defined as the estimation rating score problem and the binary classification problem. The NCF + GMF model showed also high performance in the top N recommendation.

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References

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