Beyond lists: studying the effect of different recommendation visualizations

Recommendation Systems have been studied from several perspectives over the last twenty years --prediction accuracy, algorithmic scalability, knowledge sources, types of recommended items and tasks, evaluation methods, etc.-- but one area that has not been deeply investigated is the effect of different visualizations and their interaction with personal traits on users' evaluation of the recommended items. In this paper, I survey visual approaches that go beyond presenting the recommended items as a textual list or as annotations in context. I also review related literature from recommendations' explanations. In this thesis, I aim to understand how different visualizations and some personal traits might influence users' assessment of recommended items, particularly in domains where multidimensional data or contextual constraints are involved. I present the prototype of 2 recommendation visualizations and then briefly propose the research approach of this investigation.