Improving Accuracy of Movie Recommender System Using Word2Vec and Deep Neural Networks

Kang, BooSik

doi:10.34163/jkits.2018.13.5.006

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.

Citation status

KCI Citation Counts (3)
열림/닫힘 버튼
REFERENCES (19)
열림/닫힘 버튼
* 2022년 이후 발행 논문의 참고문헌은 현재 구축 중입니다.

[confproc] B. Sarwar / 2001 / Item-based collaborative filtering recommendation algorithms / Proceedings of the 10th International Conference on World Wide Web : 285~295

[journal] K. Goldberg / 2001 / EigenTaste: a constant time collaborative filtering algorithm / Information Retrieval 4(2) : 133~151

[journal] 강부식 / 2017 / The Study of Recommendation Algorithm's Predictive Accuracy Improvement Using Structural Holes on Trust-based Social Networks / 한국지식정보기술학회 논문지 / 한국지식정보기술학회 12(1) : 209~217 / 10.34163/jkits.2017.12.1.019

[journal] 강부식 / 2018 / Improving Predictive Accuracy of User-based Collaborative Filtering Using Word2Vec / 한국지식정보기술학회 논문지 / 한국지식정보기술학회 13(1) : 169~176 / 10.34163/jkits.2018.13.1.017

[journal] 김정미 / 2017 / Text Document Classification Based on Recurrent Neural Network Using Word2vec / 한국지능시스템학회 논문지 / 한국지능시스템학회 27(6) : 560~565

[journal] 허찬 / 2017 / A Novel Method for Constructing Sentiment Dictionaries using Word2vec and Label Propagation / 한국차세대컴퓨팅학회 논문지 / 한국차세대컴퓨팅학회 13(2) : 93~101

[journal] 김도우 / 2017 / Categorization of Korean News Articles Based on Convolutional Neural Network Using Doc2Vec and Word2Vec / 정보과학회논문지 / 한국정보과학회 44(7) : 742~747

[journal] 김우주 / 2016 / Semantic Extention Search for Documents Using the Word2vec / 한국콘텐츠학회 논문지 / 한국콘텐츠학회 16(10) : 687~692 / http://dx.doi.org/10.5392/JKCA.2016.16.10.687

[journal] 안주영 / 2015 / A Study of 'Emotion Trigger' by Text Mining Techniques / 지능정보연구 / 한국지능정보시스템학회 21(2) : 69~92

[journal] T. Mikolov / 2013 / Distributed representations of words and phrases and their compositionality / Advances in Neural Information Processing Systems 26 : 3111~3119

[confproc] O. Barkan / 2016 / Item2vec:neural item embedding for collaborative filtering / 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing : 1~6

[thesis] V. Kuzmin / 2017 / Item2vec-based approach to a recommender system / University of Tartu

[confproc] G. S. Jeon / 2017 / Word2vec based collaborative filtering for movie rating prediction / Korea Software Congress : 844~846

[other] R. Devooght / 1608 / Collaborative filtering with recurrent neural networks / arXiv preprint arXiv:1608.07400

[journal] 권명하 / 2018 / Improving Recurrent Neural Network based Recommendations by Utilizing Embedding Matrix / 정보과학회논문지 / 한국정보과학회 45(7) : 659~666

[confproc] G. Guo / 2013 / A novel bayesian similarity measure for recommender systems / Proceedings of the 23th International Joint Conference on Artificial Intelligence : 2619~2625

[web] / 2017 / Gensim / https://radimrehurek.com/gensim/

[journal] B. T. Zhang / 2015 / Deep hypernetwork models / Communications of the Korean Institute of Information Scientists and Engineers 33(8) : 11~24

[web] / 2016 / Librec / http://www.librec.net/datasets.html

KJC Korea
Journal Central

Statistics

Tools

Sharing

Issue List

Citation status