Automated Case Generation for Recommender Systems Using Knowledge Discovery Techniques

Abstract

One approach to product recommendation in ecommerce is collaborative filtering, which is based on data of users’ consumption of assets. The alternative case-based approach is based on a more semantically rich representation of users and assets. However, generating these case representations can be a significant overhead in system development. In this paper we present an approach to case authoring based on data mining methods. Specifically, we focus on clustering algorithms. Having demonstrated the feasibility of this approach, we go on to consider what benefits such techniques might confer on the recommendation system. In this context we distinguish three levels of interpretability of cluster formations or concepts, and go on to argue that, while the first two levels offer no immediate advantages over each other in the recommendation domain, moving to the third level allows us to overcome the bootstrap problem of recommending assets to new users.