Prowess Improvement of Accuracy for Moving Rating Recommendation System

Authors

  • P. Damodharan  Department of Computer Science and Engineering, Akshaya College of Engineering and Technology, Coimbatore, Tamil Nadu, India
  • Dr. C. S. Ravichandran  Department of Electrical and Electronics Engineering, Sri Ramakrishna Engineering College, Coimbatore, Tamil Nadu, India

Keywords:

Recommender System, Recommendation Stability, Iterative Smoothing, Singular Value Decomposition And Naive Bayes Classification.

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.

References

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International Journal of Scientific Research in Science and Technology

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Published

2017-12-30

Issue

Volume 3, Issue 1, January-February-2017

Section

Research Articles

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How to Cite

[1]
P. Damodharan, Dr. C. S. Ravichandran "Prowess Improvement of Accuracy for Moving Rating Recommendation System" International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011,Volume 3, Issue 1, pp.631-635, January-February-2017.