Personalized Recommendation for Digital Library using Gaussian Mixture Model
Jianhui Cao, Yanlin Guo, Chunxiang Dong, Pengru Liu · Journal of Networks · 2014
Motivated by the rapid development of information technique, digital resources have become one of the main sources for obtaining information data. Digital library, as the most popular way for obtaining information resources in the network and so on, provides a steady flow of information resources for people on the internet. Further, we need to provide personalized recommendation service to users for a recommendation system of digital library research. In the actual system, the sparseness of actual data has a direct influence on the accuracy of the recommendation results. The present methods only improve the accuracy of the recommendation results in a certain extent, but and they cannot be used to solve the problem of caused by data sparseness. Therefore, to overcome the limitation of previous methods and develop a more robust method, this paper is devoted to a personalized recommendation algorithm based on Gaussian mixture model clustering technique. The research on personalized recommendation has three steps:(1) select probability density function;(2) estimate all the parameters of the Gaussian distribution; (3) calculate the feature words of users interested and the similarity between index database and books. The experiment results show that the algorithm can effectively improve the accuracy of personalized recommendation