How Pento modeled aesthetic taste with Qdrant

Pento has developed an innovative recommendation system using Qdrant, aiming to connect people based on shared aesthetic tastes in art rather than popularity. By transforming user-art interactions into a semantic vector space, the system maps these preferences into clusters through Qdrant's recommendation API. Unlike traditional recommenders, which often rely on popularity and collaborative filtering, Pento's approach captures the dynamic and multifaceted nature of aesthetic taste by treating each user's interactions as evolving clusters. These clusters are scored based on recency and interaction frequency to maintain a dynamic representation of a user's current preferences. The system leverages these clusters, represented by medoids, to form a multivector profile that captures both the user's positive and negative aesthetic affinities. Using Qdrant's capabilities, the system matches users with artists whose styles align with their current tastes while filtering out those that do not. This method also addresses the cold start problem by quickly adapting to new users through early interactions with a diverse range of artworks, enabling immediate and relevant recommendations across various domains.