Attribute aware anonymous recommender systems

Abstract

Anonymous recommender systems are the electronic pendant to vendors, who ask the customers a few questions and subsequently recommend products based on the answers. In this article we will propose attribute aware classifier-based approaches for such a system and compare it to classifier-based approaches that only make use of the product IDs and to an existing real-life knowledge-based system. We will show that the attribute-based model is very robust against noise and provides good results in a learning over time experiment.