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Traditionally, recommender systems for the Web deal with applications thathave two dimensions, users and items. Based on access logs that relate thesedimensions, a recommendation model can be built and used to identify a set of Nitems that will be of interest to a certain user. In this paper we propose amethod to complement the information in the access logs with contextualinformation without changing the recommendation algorithm. The method consistsin representing context as virtual items. We empirically test this method withtwo top-N recommender systems, an item-based collaborative filtering techniqueand association rules, on three data sets. The results show that our method isable to take advantage of the context (new dimensions) when it is informative.

Using Contextual Information as Virtual Items on Top-N Recommender Systems