. Puc, Departamento Ciencia de la Computación
Recommendation Systems for Visual Art
Sala 1, Edificio Rolando Chuaqui (dep. Matematicas)
Recommender Systems (RecSys) help people to find relevant items within a large information space by learning user preferences and producing personalized suggestions. Recsys are a well established research area, but most works have focused on recommendation
of movies, music or books. In this talk, I will present a survey of visual recommender systems, which use images
either as a recommendation target or as a signal to model user preferences towards suggesting other types of items.
Then, I will focus on recommendation of visual art, where I will introduce a deep neural architecture for personalized recommendation
of paintings: CuratorNet. Our experiments indicate that this network performs especially well upon visual one-of-a-kind artworks, i.e.,
items with a single instance which do not allow the use traditional collaborative filtering methods. Finally, I will introduce
and discuss some ideas for future work relating to recent trends in creative deep learning for art, such as style transfer.