한국컴퓨터정보학회논문지 (Journal of the Korea Society of Computer and Information)

  • 제21권7호
  • /
  • Pages.9-16
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  • 2016
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  • 1598-849X(pISSN)
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  • 2383-9945(eISSN)
한국컴퓨터정보학회 (Korean Society of Computer Information)
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Addressing the New User Problem of Recommender Systems Based on Word Embedding Learning and Skip-gram Modelling

  • Shin, Su-Mi (Dept. of Computer Engineering, Hongik University / Dept. of Information Service, KISTI) ;
  • Kim, Kyung-Chang (Dept. of Computer Engineering, Hongik University)
  • 투고 : 2016.06.20
  • 심사 : 2016.07.09
  • 발행 : 2016.07.29
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초록

Collaborative filtering(CF) uses the purchase or item rating history of other users, but does not need additional properties or attributes of users and items. Hence CF is known th be the most successful recommendation technology. But conventional CF approach has some significant weakness, such as the new user problem. In this paper, we propose a approach using word embedding with skip-gram for learning distributed item representations. In particular, we show that this approach can be used to capture precise item for solving the "new user problem." The proposed approach has been tested on the Movielens databases. We compare the performance of the user based CF, item based CF and our approach by observing the change of recommendation results according to the different number of item rating information. The experimental results shows the improvement in our approach in measuring the precision applied to new user problem situations.

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참고문헌

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