TindART at ACM MM 2020

We present TindART - a comprehensive visual arts recommender system. TindART leverages real time user input to build a user-centric preference model based on content and demographic features.

System Overview

Our system is coupled with visual analytics controls that allow users to gain a deeper understanding of their art taste and further refine their personal recommendation model. The content based features in TindART are extracted using a multi-task learning deep neural network which accounts for a link between multiple descriptive attributes and the content they represent.

Our demographic engine is powered by social media integrations such as Google, Facebook and Twitter profiles the users can login with. Both the content and demographics power a recommender system which decision making process is visualized through our web t-SNE implementation.