IJMTES – SHREWD INTENSIFICATION OF ACCURACY FOR MOVING RATING RECOMMENDATION SYSTEM

Journal Title : International Journal of Modern Trends in Engineering and Science

Author’s Name : G P Karpagam  unnamed

Volume 03 Issue 06 2016

ISSN no:  2348-3121

Page no: 108-111

Abstract – Online readers require tools to help them cope with the enormous  of content available on the world-Wide Web. Selections are made by readers in traditional media with the help of assistance. Recommender system based on web data mining is very useful, more exact and provides worldwide services to the user. Recommender systems analyze patterns of user interest in items or products to provide  recommendations for items that will suit a user’s taste. This includes both implicit intervention in the form of editorial oversight and explicit aid in the form of recommendation services such as movie reviews and restaurant guides. Several opportunities are provided by the electronic medium to offer recommendation services, ones that adapt over time to trace their evolving interests. Both content-based and collaborative systems can provide such a examine, but individually they both face shortcomings. To improve the stability various techniques are used .Main proposal of the project is the Singular value  decomposition and Naive bayes classification to increase the stability.

KeywordsRecommender system ,recommendation stability, iterative smoothing, Singular value  decomposition and Naive bayes classification 

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