A Bray-Curtis strategy for boosting product recommendation

Collaborative filtering (CF) is one of the most successful recommender techniques. It is based on the idea that people often get the best recommendations from someone with similar tastes to themselves. Broadly, there are memory-based and model-based CF techniques. As a representative memorybased CF technique, neighborhood-based CF uses some measure to compute the similarity between users. In model-based CF, clustering algorithms group users according to their ratings and use the cluster as its neighborhood. A shortcoming of all these methods is the over exploitation of data locality. These methods discard the use of the global data structure affecting recall and diversity. To address this limitation, we propose to explore the use of a spectral clustering strategy to infer the user cluster structure. Then, to expand the search of relevant users/items we use the Bray-Curtis coefficient, a measure that is able to exploit the global cluster structure to infer user proximity. Compared to traditional similarity metrics our approach is more flexible because it can capture relationships considering the overall cluster structure, enriching recommendations. We perform an experimental comparison of the proposed method against traditional prediction algorithms using three widely known benchmark data sets. Our experimental results show that our proposal is feasible.