Research On Hybrid Personalized Recommendation Algorithms

With the development of the network technique and Internet, people’s life become more convenient and colorful. But the Internet also brings people the information over-loading problem. It is hard that people retrieve useful information without the information filtering tools. Traditional search engine can only solve part of this problem, because the search engine has high requirement of users, and give back too much and complex information. Also the search engine can’t give the personal result to the users. So the recommendation system is born to make a further progress. Based the users’long interest and behaviour, the recommendation system give the corresponding information to the users.In this paper we proposed a hybrid personal recommendation algorithm for improving the accuracy of the recommendation algorithms. First we discuss the global and local feature of recommendation system, then analyse the global and local property in the users’behavior and classic recommendation algorithms. We build a hybrid personal recommendation model which combines the global the recommendation algorithm and the local recommendation algorithm, resulting in better accuracy in prediction.In the latter, we deliberately described our experiment approaches, and the experiment processes on the popular testing data set called Movielens. The results indicate the useful of our algorithm.