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Enhanced Swarm based Optimized Recommendation System

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Authors:
Shivani Sharma
10.5120/ijca2016911720

Shivani Sharma. Enhanced Swarm based Optimized Recommendation System. International Journal of Computer Applications 152(5):15-19, October 2016. BibTeX

@article{10.5120/ijca2016911720,
	author = {Shivani Sharma},
	title = {Enhanced Swarm based Optimized Recommendation System},
	journal = {International Journal of Computer Applications},
	issue_date = {October 2016},
	volume = {152},
	number = {5},
	month = {Oct},
	year = {2016},
	issn = {0975-8887},
	pages = {15-19},
	numpages = {5},
	url = {http://www.ijcaonline.org/archives/volume152/number5/26314-2016911720},
	doi = {10.5120/ijca2016911720},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

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 K-mean 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.

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Keywords

Data Mining, Recommendation system, KNN, PSO, Precision, Recall