Research In Personalized Information Recommendation Based On Social Tagging

Information available on internet grows far more rapidly than our ability to process it.Recommender System is one of promising technology to help us find most valuable information without explicit query.It provides recommendations based on user's history behavior and preferences to entities.This paper puts forward a method based on social tagging clustering method, with Belonging Coefficient matrix instead of score matrix, can not only solve data sparseness, but also can largely reduce data dimension. SVD(Singular Value Decomposition) has similar dimension reduction with the recommended algorithms, the thoughts in complexity and recommend effect has certain advantages. It is recommended to traditional methods of improving the traditional method, which can solve the problem is more onefold interest model. And this method also narrowed score matrix scale, improve the computational efficiency.Based on such MovieLens, Amazon and Netflix data sets based on the experiments show that the mass marked with traditional personalized recommendation algorithm based on user similarity analysis method, this paper, we may conclude that the algorithm can significantly improve recommend effect. Taking the theory of constructivism as the theory base.