This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology
Exploiting Additional Dimensions as Virtual Items on Top-N Recommender Systems
Lyon, France
August 22-August 27
ISBN: 978-0-7695-4513-4
Traditionally, recommender systems for the web deal with applications that have two dimensions, users and items. Based on access data that relate these dimensions, a recommendation model can be built and used to identify a set of N items that will be of interest to a certain user. In this paper we propose a multidimensional approach, called DaVI (Dimensions as Virtual Items), that enables the use of common two-dimensional top-N recommender algorithms for the generation of recommendations using additional dimensions (e.g., contextual or background information). We empirically evaluate our approach with two different top-N recommender algorithms, Item-based Collaborative Filtering and Association Rules based, on two real world data sets. The empirical results demonstrate that DaVI enables the application of existing two-dimensional recommendation algorithms to exploit the useful information in multidimensional data.
Index Terms:
Recommender systems, multidimensional recommender systems, multidimensional data
Citation:
Marcos Aurélio Domingues, Alípio M´rio Jorge, Carlos Soares, "Exploiting Additional Dimensions as Virtual Items on Top-N Recommender Systems," wi-iat, vol. 1, pp.92-95, 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, 2011
Usage of this product signifies your acceptance of the Terms of Use.