TindART was a visual arts recommender system I co-developed, published at ACM Multimedia 2020.
Highlights
- Published research: Co-authored a paper at ACM Multimedia 2020 (A* conference) with the University of Amsterdam
- Recommender system: Built a personal visual arts recommender that learns user taste through interaction
- Visual analytics: Co-developed a comprehensive analytics tool to explore and understand recommendation patterns
- Full system: Delivered an end-to-end system combining ML recommendations with an interactive frontend
TindART was a project at the University of Amsterdam where I was part of a team building a personal visual arts recommender system paired with a visual analytics tool. We worked under the guidance of PhD candidate Gjorgji Strezoski and professor Marcel Worring.
The system
The idea was to build a recommender that could learn a user’s taste in visual art through interaction and surface relevant works. The system combined recommendation algorithms (using LightFM for collaborative filtering) with a visual analytics interface built in D3.js that let users explore why certain works were being recommended. RabbitMQ and Celery handled the async processing pipeline.
The result
The like module — swipe to teach the system your taste.
The analytics map — user preferences visualized across the collection.
The project led to a paper published at ACM Multimedia 2020, one of the top conferences in multimedia research. It was a meaningful intersection of the two things I care about: building real interactive systems and applying machine learning to solve a concrete problem.