Tell me what you like and ill tell you what you are: Discriminating visual preferences on Flickr data.

The John Ruskin\u2019s 19th century adage suggests that personal taste is not merely an absolute set of aesthetic principles valid for everyone: actually, it is a process of interpretation which have also roots in one\u2019s life experiences. This aspect represents nowadays a major problem for inferring automatically the quality of a picture. In this paper, instead of trying to solve this age-old problem, we consider an intriguing, orthogonal direction, aimed at discovering how different are the personal tastes. Given a set of preferred images of a user, obtained from Flickr, we extract a pool of low- and high-level features; LASSO regression is then exploited to learn the most discriminative ones, considering a group of 200 random Flickr users. Such aspects can be easily recovered, allowing to understand what is the \u201cwhat we like\u201d which distinguish us from the others. We then perform multi-class classification, where a test sample is a set of preferred pictures of an unknown user, and the classes are all the users. The results are surprising: given only 1 image as test, we can match the user preferences definitely more than the chance, and with 20 images we reach an nAUC of 91%, considering the cumulative matching characteristic curve. Extensive experiments promote our approach, suggesting new intriguing perspectives in the study of computational aesthetics