Title :
Improved Collaborative Filtering Algorithm in the Research and Application of Personalized Movie Recommendations
Author :
Xiao Peng ; Shao Liangshan ; Li Xiuran
Author_Institution :
Liaoning Tech. Univ., Huludao, China
Abstract :
With the development of the Internet and e-commerce, recommendation system has been widely used. In this paper, the electronic commerce recommendation system, has a further study and focuses on the collaborative filtering algorithm in the application of personalized movie recommendation system. According to the characteristics of movie recommendation system itself and traditional collaborative filtering algorithm of sparse user ratings matrix,this paper is proposed based on a hybrid user-based and item-based collaborative filtering algorithms, and applied to the MovieLens dataset, achieved good effect.
Keywords :
collaborative filtering; electronic commerce; entertainment; recommender systems; sparse matrices; MovieLens dataset; electronic commerce recommendation system; item-based collaborative filtering algorithm; personalized movie recommendation system; sparse user ratings matrix; user-based collaborative filtering algorithm; Collaboration; Films; Filtering; Filtering algorithms; Motion pictures; Prediction algorithms; Training; Chinese famous tea; Least square support vector machine; Principal component analysis; hyperspectral imaging technique;
Conference_Titel :
Intelligent Systems Design and Engineering Applications, 2013 Fourth International Conference on
Conference_Location :
Zhangjiajie
Print_ISBN :
978-1-4799-2791-3
DOI :
10.1109/ISDEA.2013.483