The Reasearch Of Recommendation Algorithm Based On Network

With the rapid development of Internet technology, the servers which connect to Internet is become more and more, and it is more convenient for sharing information. At the same time, the data scale on the Internet is on exponential growth, and bring about the problem of data storage and data management. In the face of such mass of data, users find that it is become much more difficult to find the product of information which they interested in. The traditional searching algorithm can only present the same search result to users by the key words they offered, but the usage of information is very low, which called information overloaded. One of the most effective way to solve the problem of information overloaded is to use personalized recommendation system. Personalized recommendation system is produced to lower the time cost of users to searching useful information, present the information to users as a way of "push". Which is achieved by the method using the history records of users, which include browsing records, click records and so on, and then utilize some kinds of recommendation algorithms, guess the hobbies and interests of users and forcast which information they may be interested in. Thus recommend products to users. This article make some general understanding on recommendation system first, and then make deep research on the development of recommendation systems based on network, and introduce the recommendation algorithm based on bipartite network in detail. On the basis of recommendation algorithm based on bipartite network, we make three aspects achievements as follows:(1) This article use Hadoop platform as bottom storage and distribute computing framework, and mongodb as auxiliary storage, and transform the resource allocation algorithm based on bipartite network to mapreduce mode, and design a parallelized recommendation algorithm based on resource allocation algorithm of bipartite network. At last we design several experiments and the results of these experiments prove the excellent efficiency of our parallelized algorithm.(2) This article analyse the users’interests transference in product choose progress, and the influence of recommendation results by such transference. Based on these analysis, this article take consideration of the factor of time, and design an improved recommendation algorithm based on resource allocation algorithm of bipartite network. And at last we design several experiments to prove the excellent efficiency of our parallelized algorithm.(3) Resource allocation algorithm of bipartite network put the preference relation of users and products include the edges of network, without the consideration of how much the user prefer to the product. This article put the rating information to the network as edges’weight. And improve the resource allocation method of the resource allocation algorithm. And then design an improved weighted recommendation algorithm based on resource allocation algorithm of bipartite network. And at last we design several experiments to prove the excellent efficiency of our parallelized algorithm.