Data filtering tools leveraging machine learning (ML) algorithms have been revolutionizing the business and tech industries for years. Benjamin Gulak, a professional painter, entrepreneur, and computer scientist, made it his mission to ensure that the impact of these cutting-edge technologies reaches the art sector. He envisioned a platform that would reshape the art industry by facilitating direct connections between artists and enthusiasts worldwide to democratize access to art.
Ben turned this vision into reality by developing the Networked Artistic Learning Algorithm (NALA), an innovative platform that bridges the gap between artists, and art lovers. NALA boasts a state-of-the-art recommender engine built upon the world's largest fine art database. The hybrid recommender employs collaborative and content filtering in addition to deep learning algorithms to find patterns from various data sources (i.e., in-app user activity, global market trends. etc.).
