Finding and analysing good neighbourhoods to improve collaborative filtering

The research community has historically addressed the collaborative filtering task in several fashions. Although model-based approaches such as matrix factorisation attract substantial research efforts, neighbourhood-based recommender systems are effective and interpretable techniques. The performance of neighbour-based methods is strongly tied to the clustering strategies. In this paper, we show that there is room for improvement in this type of recommenders. For showing that, we build an oracle which yields approximately optimal neighbourhoods. We obtain ground truth neighbourhoods using the oracle and perform an analytical study of those to characterise them. As a result of our analysis, we propose to change the user profile size normalisation that cosine similarity employs in order to improve the neighbourhoods computed with k-NN algorithm. Additionally, we present a more appropriate oracle for current grouping strategies which leads us to include the IDF effect on the cosine formulation. An extensive experimentation on four datasets shows an increase in ranking accuracy, diversity and novelty using these cosine variants. This work shed light on the benefits of this type of analysis and paves the way for future research in the characterisation of good neighbourhoods for collaborative filtering.