A parameter-free KNN for rating prediction

Abstract

Among the most popular collaborative filtering algorithms are methods based on the K nearest neighbors (KNN). In their basic operation, KNN methods consider a fixed number of neighbors to make recommendations. However, it is not easy to choose an appropriate number of neighbors. Thus, it is generally fixed by calibration to avoid inappropriate values which would negatively affect the accuracy of the recommendations.In the literature, some authors have addressed the problem of dynamically finding an appropriate number of neighbors. But they use additional parameters which limit their proposals because these parameters also require calibration. In this paper, we propose a parameter-free KNN method for rating prediction. It is able to dynamically select an appropriate number of neighbors to use. The experiments that we did on four publicly available datasets demonstrate the efficiency of our proposal. It rivals those of the state of the art in their best configurations.