Addressing the New User Problem of Recommender Systems Based on Word Embedding Learning and Skip-gram Modelling (새로운 사용자 문제 개선을 위한 워드임베딩 학습과 Skip-gram 모델링 기반의 추천 시스템)

신수미; KIM KYUNG CHANG (김경창)

Addressing the New User Problem of Recommender Systems Based on Word Embedding Learning and Skip-gram Modelling

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2016, 21(7), pp.9-16
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science

신수미 ¹, KIM KYUNG CHANG ²

¹한국과학기술정보연구원
²홍익대학교

Accredited

ABSTRACT

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.

KEYWORDS

Recommender system, Word Embedding, Skip-gram, New User Problem

Citation status

* References for papers published after 2023 are currently being built.

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Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65