Journal of Information Technology Applications and Management

Korea Data Strategy Society (한국데이터전략학회)
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A Study for GAN-based Hybrid Collaborative Filtering Recommender

GAN기반의 하이브리드 협업필터링 추천기 연구

  • Received : 2022.11.23
  • Accepted : 2022.12.23
  • Published : 2022.12.31
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Abstract

As deep learning technology in natural language and visual processing has rapidly developed, collaborative filtering-based recommendation systems using deep learning technology are being actively introduced in the recommendation field. In this study, OCF-GAN, a hybrid collaborative filtering model using GAN, was proposed to solve the one-class and cold-start problems, and its usefulness was verified through performance evaluation. OCF-GAN based on conditional GAN consists of a generator that generates a pattern similar to the actual user preference pattern and a discriminator that tries to distinguish the actual preference pattern from the generated preference pattern. When the training is completed, user preference vectors are generated based on the actual distribution of preferred items. In addition, the cold-start problem was solved by using a hybrid collaborative filtering recommendation method that additionally utilizes user and item profiles. As a result of the performance evaluation, it was found that the performance of the OCF-GAN with additional information was superior in all indicators of the Top 5 and Top 20 recommendations compared to the existing GAN-based recommender. This phenomenon was more clearly revealed in experiments with cold-start users and items.

Keywords

Acknowledgement

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

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