A case study of exploiting data mining techniques for an industrial recommender system

Abstract

We describe a case study of the exploitation of Data Mining techniques for creating an industrial recommender system. The aim of this system is to recommend items of a fashion retail store chain in Spain, producing leaflets for loyal customers announcing new products that they are likely to want to purchase. Motivated by the fact of having little information about the customers, we propose to relate demographic attributes of the users with content attributes of the items. We hypothesise that the description of users and items in a common content-based feature space facilitates the identification of those products that should be recommended to a particular customer. We present a recommendation framework that builds Decision Trees for the available demographic attributes. Instead of using these trees for classification, we use them to extract those content-based item attributes that are most widespread among the purchases of users who share the demographic attribute values of the active user. We test our recommendation framework on a dataset with oneyear purchase transaction history. Preliminary evaluations show that better item recommendations are obtained when using demographic attributes in a combined way rather than using them independently.