Understanding Similarity Metrics in Neighbour-based Recommender Systems

Neighbour-based collaborative filtering is a recommendation technique that provides meaningful and, usually, accurate recommendations. The method’s success depends however critically upon the similarity metric used to find the most similar users (neighbours), the basis of the predictions made. In this paper, we explore twelve features that aim to explain why some user similarity metrics perform better than oth-ers. Specifically, we define two sets of features, a first one based on statistics computed over the distance distribution in the neighbourhood, and, a second one based on the near-est neighbour graph. Our experiments with a public dataset show that some of these features are able to correlate with the performance up to a 90%