Communications of the ACM
Machine Learning
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
Incorporating contextual information in recommender systems using a multidimensional approach
ACM Transactions on Information Systems (TOIS)
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Dimensions as Virtual Items: Improving the predictive ability of top-N recommender systems
Information Processing and Management: an International Journal
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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.