Actively building collaborative filtering recommendation in clustered social data

A modified collaborative filtering recommendation has been put forward for clustered data in social networks. Firstly, the basic model is built up, which shows the outline of current existing types of the recommendation platform. Secondly, on this basis, the vertexes and edges in the model are divided as the entities of the social networks with properties given formally. This framework implements traditional collaborative filtering based on clustered datasets using improved k-means clustering method, and our design has put forward to picking up the active users among all of them and let them help our system generate recommendations more precise and faster. Finally, we have crawled through data from two famous movie recommendation websites, MovieLens and Imdb, in the experiment test. From the experiment results, it is concluded that the implemented collaborative filtering recommendation system is performed better than the naive one both in precision and stability, which aims to supply collaborative filtering recommendation and actively contributing recommendation that suits users´ tastes, has completed the task of improving collaborative recommendation and reached the expected goals.