Clustering Users by exploiting Activity Tracks in Recommender Systems for SME

Personalized recommendation of products is an essential feature in any e-commerce service and is becoming more and more important for SME as well. The main problem is to be able to exploit limited amount of data concerning both user interactions and item availability. In the present paper, we introduce and evaluate some k-means based clustering strategies able to recognize different user categories even in presence of limited data. The ability to recognize users on the basis of their activity on the e-commerce site is fundamental to get insights about their preferences, in such a way that suitable products (or categories of products) can be recommended to them. We report on the experiments we have performed in the evaluation of three different clustering strategies, having the goal of grouping users showing similar behavior in interacting with items. We conclude that a standard k-means based on the Frobenius norm of the user matrix can provide good performances in terms of clustering users in the corresponding categories of interest.