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Ranking Technique to Improve Diversity in Recommender Systems

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International Journal of Computer Applications
© 2013 by IJCA Journal
Volume 68 - Number 2
Year of Publication: 2013
Authors:
Vaishnavi. S
Jayanthi. A
Karthik. S
10.5120/11551-6828

Vaishnavi. S, Jayanthi. A and Karthik. S. Article: Ranking Technique to Improve Diversity in Recommender Systems. International Journal of Computer Applications 68(2):20-24, April 2013. Full text available. BibTeX

@article{key:article,
	author = {Vaishnavi. S and Jayanthi. A and Karthik. S},
	title = {Article: Ranking Technique to Improve Diversity in Recommender Systems},
	journal = {International Journal of Computer Applications},
	year = {2013},
	volume = {68},
	number = {2},
	pages = {20-24},
	month = {April},
	note = {Full text available}
}

Abstract

The role E-marketing became important in everyday life. To help them to give correct products to human users, Recommender Systems have been evolved. It helps to find products related to user's interest. Mostly these systems employ collaborative filtering technique through which like minded users can be found. The key challenge is to attain quality in recommendations namely accuracy, diversity, novelty etc. Major algorithms have been focused on improving accuracy but diversity is important to have qualitative recommendations. On achieving diversity, distinct categories of items are taken for giving recommendations. Coverage is the main metric here. This paper gives one way to increase diversity by using LCM (Linear time Closed item set Miner) version 2 and I-Tree (Item-set tree). Data mining techniques are widely used in Recommender systems.

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