Research On Hybrid Algorithm Based On Item - User Hybrid Collaborative Filtering

The arrival of the era of network, take us into the information age, in such a context filled with limitless opportunities and challenges. With the continuous development of information technology, information communication more smooth, the information in the explosive growth, the era of big data came to us. Information overload is a growing problem, how people make choices in the face of such a wealth of information before, which filter out useless to us, are not interested in taking the time to know the information, and now we have been called to rush to solve the problem. How to make your computer can help us solve this problem, the decision to provide personalized service, has become a hot spot for scientific research. Personalized recommendation system is timely made, and has become an important means to solve problems in the field.The collaborative filtering algorithm, which is classical algorithm of recommendation system, and we have done many work about it. In the historical record of the behavior of the user, which is the user score matrix.Through the matrix to discover similarity users, items, and in order to calculate the target users of the project did not score prediction score. There are many of problems in traditional collaborative filtering algorithms:scoring matrix sparsely, cold start issues in new users or new items, real-time and many other issues. To solve these problems, this paper proposes a hybrid based on item-user collaborative filtering recommendation algorithm. Rating prediction by filling the improved algorithm effectively improve the user rating matrix sparsely; combined clustering algorithm effectively improve the real-time algorithm; finally, item-user double recommendation from the filter, effectively solve the cold start problem.