Map-based Visualization of Item Spaces for Increasing Transparency and Control in Recommender Systems

Abstract

Recommender systems (RS) are very common tools designed to help users choose items from a large number of alternatives. While their algorithms are already quite mature in terms of precision, RS cannot unfold their full potential due to a lack of transparency and missing means of control. In this paper we introduce a method aiming at creating recommendations that are comprehensible and controllable by their users while granting an overview over the item domain. To achieve this, the entire item space of a domain is visualized using a map-like interface. Inside, users can express their preferences on which the RS reacts with matching recommendations. To change recommendations, users can alter their preferences expressed, which creates a continuous feedback loop between user and RS. We demonstrate our general method using two prototype applications, located in different item domains and utilizing different forms of visualization and interaction modalities. Empirical user studies with both prototypes show a great potential of our method to increase overview, transparency and control in RS.