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Application of Domain Knowledge in Transaction-based Recommender Systems through Word Embedding

트랜잭션 기반 추천 시스템에서 워드 임베딩을 통한 도메인 지식 반영

  • 최영제 (연세대학교 산업공학대학원) ;
  • 문현실 (국민대학교 경영대학원) ;
  • 조윤호 (국민대학교 경영학부 빅데이터경영통계전공)
  • Received : 2020.01.19
  • Accepted : 2020.02.26
  • Published : 2020.03.31

Abstract

In the studies for the recommender systems which solve the information overload problem of users, the use of transactional data has been continuously tried. Especially, because the firms can easily obtain transactional data along with the development of IoT technologies, transaction-based recommender systems are recently used in various areas. However, the use of transactional data has limitations that it is hard to reflect domain knowledge and they do not directly show user preferences for individual items. Therefore, in this study, we propose a method applying the word embedding in the transaction-based recommender system to reflect preference differences among users and domain knowledge. Our approach is based on SAR, which shows high performance in the recommender systems, and we improved its components by using FastText, one of the word embedding techniques. Experimental results show that the reflection of domain knowledge and preference difference has a significant effect on the performance of recommender systems. Therefore, we expect our study to contribute to the improvement of the transaction-based recommender systems and to suggest the expansion of data used in the recommender system.

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