Explaining Online Recommendations Using Personalized Tag Clouds

Abstract

Recommender systems are sales-supporting applications that are usually integrated into online shops and are designed to point the visitor to products or services she or he might be interested in but has not bought yet. In the last decade, many techniques have been developed to improve the predictive accuracy of such systems. However, there are also factors other than accuracy that infl uence the user-perceived quality of such a system. In particular, system-generated explanations as to why a certain item has been recommended have shown to be a valuable tool to improve both the user's satisfaction and the system's effi ciency. This paper reports the results of a fi rst user study which was conducted to evaluate whether personalized tag clouds are an appropriate means to visually explain recommendations. The evaluation reveals that using tag clouds as explanation mechanism leads to higher user satisfaction and recommendation effi ciency than previous keyword-style explanations.