NDLI: Statistical Modeling of Diversity in Top-N Recommender Systems

Content Provider	IEEE Xplore Digital Library
Author	Zhang, Mi Hurley, Neil
Copyright Year	2009
Abstract	This paper provides the first statistical analysis of recommendation diversity. We propose a model that allows diversity to be evaluated analytically using the concentration index, a statistical measure of diversity. While this model abstracts the recommendation process, it provides good overall agreement with real recommendation algorithms. Using the model we are able to analytically demonstrate the trade-off between diversity and overall system performance. Moreover, the model makes explicit the various choices that are available to the algorithm designer to improve recommendation diversity. Our exploration of these choices provides good insight into what can be achieved in practice by algorithms that attempt to provide greater recommendation diversity without significant degradation of system precision.
Starting Page	490
Ending Page	497
File Size	440730
Page Count	8
File Format	PDF
ISBN	9780769538013
DOI	10.1109/WI-IAT.2009.83
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-09-15
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis collaborative Predictive models Educational institutions Paper technology Intelligent agent Computer science top-N recommendation diversity Abstracts statistical modeling Performance analysis Informatics Recommender systems
Content Type	Text
Resource Type	Article

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