Experimental results for considering Virtual Near Neighbors in Collaborative Filtering’s Rating Prediction

Dionisis Margaris, Dionysios Vasilopoulos, Costas Vassilakis, Dimitris Spiliotopoulos
SoDa Technical report TR-19001

In this technical report, we present the experimental findings from applying an algorithm that considers virtual near neighbors (VNNs) in the rating prediction formulation process, in order to increase coverage in the context of sparse datasets.
To this end, the algorithm is applied to seven sparse datasets, which are widely used in recommender system research. Additionally, the algorithm is applied to one dense dataset, in order to gain insight on the performance of the proposed algorithm in this class of datasets, as well.
In short, the algorithm introduces the concept of VNNs i.e. virtual users, which are created from the combination of real ones, in order to be used as candidate NNs in the rating prediction computation process.
In these experiments, the optimal values for the parameters that are used in the algorithm are investigated and more specifically, the thresholds that two individual users can constitute a VNN.

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