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Diversity in Recommender System

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Author(s): Vaishali Ghanghas#1, Chhavi Rana

Journal: International Journal of Engineering Trends and Technology
ISSN 2231-5381

Volume: 4;
Issue: 6;
Start page: 2344;
Date: 2013;
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Keywords: Recommender system | collaborative filtering | diversity | dataset and control parameter.

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 also 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, including 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 cost 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 optimization problems associated with the maximization of these objectives.

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