Diversity in Recommender System

Diversity in Recommender System

  ijett-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2013 by IJETT Journal
Volume-4 Issue-6                      
Year of Publication : 2013
Authors : Vaishali Ghanghas , Chhavi Rana , Sunita Dhingra

Citation 

Vaishali Ghanghas , Chhavi Rana , Sunita Dhingra. "Diversity in Recommender System". International Journal of Engineering Trends and Technology (IJETT). V4(6):2344-2348 Jun 2013. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group.

Abstract

Recommendation system is an activated area of research that helps to allow users to find the preferable items quickly and to avoid the possible information overloads. Recommender systems use data on past user preferences to predict possible future likes and interests. A better recommender system would offer less common papers that al so draw the user’s interest. Diversity is very much related to this aspect. It generally applies to a set of items and is related to how different the items are with respect to each other. Many diverse recommendation techniques have been developed, includi ng collaborative filtering, content - based analysis. These techniques involve presenting different types of recommendation to the users which are similar in taste. The laid down work has concluded that the diversity in a set of items can be increased at a c ost of reducing system accuracy. Though, the feature of diversity is contrasting to accuracy, many researchers have tried to bring in congruence between the two. This paper present objective functions that capture the trade - off between the goals and optimi zation problems associated with the maximization of these objectives .

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Keywords
Recommender system, collaborative filtering, diversity, dataset and control parameter .