Journal of Information Technology Applications and Management

  • 제29권3호
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  • Pages.43-55
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  • 2022
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  • 1598-6284(pISSN)
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  • 2508-1209(eISSN)
한국데이터전략학회 (Korea Data Strategy Society)
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인플루언서를 위한 딥러닝 기반의 제품 추천모델 개발

Deep Learning-based Product Recommendation Model for Influencer Marketing

  • 투고 : 2022.05.17
  • 심사 : 2022.06.14
  • 발행 : 2022.06.30
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초록

In this study, with the goal of developing a deep learning-based product recommendation model for effective matching of influencers and products, a deep learning model with a collaborative filtering model combined with generalized matrix decomposition(GMF), a collaborative filtering model based on multi-layer perceptron (MLP), and neural collaborative filtering and generalized matrix Factorization (NeuMF), a hybrid model combining GMP and MLP was developed and tested. In particular, we utilize one-class problem free boosting (OCF-B) method to solve the one-class problem that occurs when training is performed only on positive cases using implicit feedback in the deep learning-based collaborative filtering recommendation model. In relation to model selection based on overall experimental results, the MLP model showed highest performance with weighted average precision, weighted average recall, and f1 score were 0.85 in the model (n=3,000, term=15). This study is meaningful in practice as it attempted to commercialize a deep learning-based recommendation system where influencer's promotion data is being accumulated, pactical personalized recommendation service is not yet commercially applied yet.

키워드

과제정보

This research was supported by the National Research Foundation of Korea(NRF) grant (No.2021R1F1A1045815)

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