Exploiting Additional Dimensions as Virtual Items on Top-N Recommender Systems
Exploiting Additional Dimensions as Virtual Items on Top-N Recommender Systems
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Date
2011
Authors
Alípio Jorge
Carlos Manuel Soares
Marcos Aurélio Domingues
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Abstract
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 twodimensional
recommendation algorithms to exploit the useful information in multidimensional data.