논문윤리하기 논문투고규정
  • 오늘 가입자수 0
  • 오늘 방문자수 40
  • 어제 방문자수 83
  • 총 방문자수 146
2018-11-14 05:43am
학회 논문지
HOME 자료실 > 학회 논문지

발간년도 : [2018]

 
논문정보
논문명(한글) [Vol.13, No.5] Improving Accuracy of Movie Recommender System Using Word2Vec and Deep Neural Networks
논문투고자 Boo-Sik Kang
논문내용 Word2Vec is a most popular method in text mining area, recently. It converts words to vectors using association among words in sentences. Similar words are nearly located in the vector space. As deep learning technology has developed rapidly, deep neural network that adopts deep learning is being applied in many areas. Improving predictive accuracy of recommender algorithms is a major work in the area of recommender systems. This study proposed an integrated method for movie recommender systems using Word2Vec and deep neural networks. First, it constructs Corpus of users and movies. It generates sentences for constructing Corpus. It finds users that give same rating to a movie, generates a sentence with those users, and constructs the Corpus of users using the sentences. It finds movies that are given same rating from an user, generates a sentence with those movies, and constructs the Corpus of movies using the sentences along the same lines. Secondly, it calculates user vectors and movie vectors using Word2Vec. Thirdly, it learns a model of deep neural networks with inputs composed of the user vectors and the movie vectors. Lastly, it recommends movies to the target user through the learned deep neural networks. To validate, the proposed method was applied to filmtrust dataset. The experimental results of 10-fold cross validation showed that the proposed method improved accuracy greatly than conventional collaborative filtering method(uCF). Also, it showed that the proposed method could solve problems with comparatively high accuracy, recommendation problems to new customers and new products, but the conventional collaborative filtering methods had difficulties recommendation to those problems.
첨부논문
   13-5-06.pdf (401.1K) [1] DATE : 2018-11-03 14:55:06