Abstract:

The basic need for this News Recommendation System is to reduce the time of any user, when he/she is in search of any particular information. When there are more amount of information in the web, then the user have to go through each type of information, to get the particular topic. But, when a user is interested in any particular topic, then the system has to recommend the information that the user will be more interested at.. In statistics, latent Dirichlet allocation (LDA) is a generative model that allows sets of  observations to be explained by unobserved  groups that explain why some parts of the data are similar. Affinity propagation is a new algorithm that takes as input measures of similarity between pairs of data points and simultaneously considers all data points as potential exemplars. Real-valued messages are exchanged between data points until a high-quality set of exemplars and corresponding clusters gradually emerges. We have used affinity propagation to solve a variety of clustering problems and we found that it uniformly found clusters with much lower error than those found by other methods, and it did so in less than one-hundredth the amount of time. Because of its simplicity, general applicability, and performance, we believe affinity propagation will prove to be of broad value in science and engineering.

Keywords: News Recommendation System, latent Dirichlet allocation (LDA), Affinity propagation