Research And Implement Of Collaborative Filtering Recommendation System Combined With Trust Model

Today, the Internet is flooded with all kinds of information, which leads to the increasingly serious problem of information overload. For ordinary users, it’s easy to be disturbed by the complex information. So they have to spend more time and energy to look for the information they need. An effective method to solve the problem of information overload is the recommendation system. Recommendation system can provide personalized recommendation services for users according to the characteristics of them. Recommendation system needs to achieve different recommendation algorithms according different requirements, and the collaborative filtering recommendation algorithm is the most popular and practical recommendation technology among all the recommendation algorithms. In this paper, we will study and improve the existing trust based collaborative filtering recommendation algorithms, and then design and implement a movie recommendation system with multiple algorithms. The main work of this paper includes:① In-depth study on recommendation system, analyze its concept and structure, evaluation standards, etc…Focus on the analysis of process, characteristics of several commonly used recommendation algorithm.② The traditional collaborative filtering recommendation algorithm has the data sparse problem which affects the accuracy of recommendation results in a great extent. In this paper, we combine the user trust model with collaborative filtering. The nearest neighbor set can be expanded by the trust relationship between different users. The data sparse problem can be alleviated by this method.③ Evaluate the improved algorithm with the Movie Lens dataset, and then compare the experimental result with some existing and correlative collaborative filtering algorithms to verify the effectiveness of the improvements.④ Design and implement a movie recommendation system. In order to cover the interest of users as much as possible, the recommendation system implements three algorithms including content based recommendation algorithm, item based collaborative filtering recommendation algorithm and the collaborative filtering recommendation algorithm combined with trust model. And the recommendation engine is realized based on the Hadoop distributed platform.