Abstract
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.
Highlights
The popularity of mobile devices makes people’s daily lives more dependent on mobile services
On the basis of the hybrid recommendation framework, this paper fully considers the efficiency of the recommender system
Where Whybrid and WSA represent the weights of two recommendation methods and ScoreSA,m represents the score of movie m derived from sentiment analysis
Summary
The popularity of mobile devices makes people’s daily lives more dependent on mobile services. Considering the usage of online information and user-generated content, collaborative filtering is supposed to be the most popular and widely deployed technique in recommender system. The similarity between users’ preference can be measured by correlation calculation In this way, users who have similar interest in movies are sorted in the same group, and movies are recommended by their reviews and ratings of movies that they have seen. The reviews of users on movies usually contain more information such as users’ preference. With the increase of the amount of data, how to provide users with high-quality recommendations quickly among the massive information has become a serious problem. A sentiment-enhanced hybrid collaborative filtering and content-based recommendation method is proposed to recommend appropriate movies to users on Spark platform.
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