Applying Distance Dynamic Qlearning Partitioning Approach in Group Recommendation System Used in Social Sites

Abstract

In this era, when people prefer online solution for most of their problems, recommendation system became the most successful tool to solve it. Various method have been introduced to improve the performance and quality of the search engine. We need a system which can design and develop the reporting system not just the latest product. Some other tools and technologies have to be merged with the recommendation system to make it more powerful and user friendly. In this paper, we have combined Group Recommendation System with Qlearning partitioning approach along with Stochastic function to increase the accuracy, precision, recall time and f-measure of the Recommendation System in social sites and the experiments has been explained by using community diagram showing different approach and tools used to make recommendation system more efficient so that it can provide the user friendly environment to the consumers.