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Wireless Communications and Mobile Computing
Volume 2018, Article ID 8263704, 9 pages
Research Article

A Sentiment-Enhanced Hybrid Recommender System for Movie Recommendation: A Big Data Analytics Framework

1School of Information, Renmin University of China, Beijing 100872, China
2Smart City Research Center, Renmin University of China, Beijing 100872, China

Correspondence should be addressed to Wei Xu; nc.ude.cur@uxiew

Received 2 December 2017; Accepted 3 January 2018; Published 22 March 2018

Academic Editor: Yin Zhang

Copyright © 2018 Yibo Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Movie recommendation in mobile environment is critically important for mobile users. It carries out comprehensive aggregation of user’s preferences, reviews, and emotions to help them find suitable movies conveniently. However, it requires both accuracy and timeliness. In this paper, a movie recommendation framework based on a hybrid recommendation model and sentiment analysis on Spark platform is proposed to improve the accuracy and timeliness of mobile movie recommender system. In the proposed approach, we first use a hybrid recommendation method to generate a preliminary recommendation list. Then sentiment analysis is employed to optimize the list. Finally, the hybrid recommender system with sentiment analysis is implemented on Spark platform. The hybrid recommendation model with sentiment analysis outperforms the traditional models in terms of various evaluation criteria. Our proposed method makes it convenient and fast for users to obtain useful movie suggestions.