Enhanced Swarm based Optimized Recommendation System

Recommendation system is needed to provide personalized information to the user to improve the searching of the user based upon their searching method. In this work, an architecture that integrates product information with user’s access log data and then generates a set of recommendations for that particular user. This architecture uses more than one data mining algorithms like clustering and pattern matching algorithms. In previous work, for Clustering they are using Kmean Clustering & other algorithm for pattern matching is Boyer Moore Pattern Matching Algorithm. To enhance this work, an optimization algorithm will be used. Here Particle Swarm optimization algorithm has been used which is a soft computing algorithm of Artificial Intelligence. It will help to improve the results on the basis of intelligence. In this work a database of mobiles is created manually and based on there technical specifications better mobiles are recommended to the user based on intelligence. It will firstly create clusters on the basis of some similarity and then based on random population and technical specifications probability will be calculated. The product with least probability will be kept on top and then sort in top to bottom order.