Evaluation of Similarity Functions by using User based Collaborative Filtering approach in Recommendation Systems

Evaluation of Similarity Functions by using User based Collaborative Filtering approach in Recommendation Systems

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2015 by IJETT Journal
Volume-21 Number-4
Year of Publication : 2015
Authors : Shaivya Kaushik, Pradeep Tomar
DOI :  10.14445/22315381/IJETT-V21P234

Citation 

Shaivya Kaushik, Pradeep Tomar"Evaluation of Similarity Functions by using User based Collaborative Filtering approach in Recommendation Systems", International Journal of Engineering Trends and Technology (IJETT), V21(4),194-200 March 2015. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

Abstract

Recommendation Systems has been comprehensively analysed and are changing from novelties used by a few E-commerce sites in the past decades.Many of the popular and largest commerce websites are widely using recommendation systems.These are popular and important part of the ecommerce ecosystem that help users to find relevant and valuable information through large product spaces.The tremendous growth of visitors and the information poses few key challenges such as producing high quality recommendation systems,performing many recommendation systems per second for millions of users and items.The paper introduce user based collaborative filtering approach and the similarity function.The algorithm will identify relationships between different users and then compute recommendation for the users.This paperpresent a most commonly used similarity functions and their computation that aims to determine which similarity function result in producing most accurate recommendation.

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Keywords
Recommendation Systems, Collaborative Filtering,Similarity Functions.