Gjorgji Strezoski
ABSTRACT
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. 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 processed is visualized through our web t-SNE implementation. TindART is live and available at: https://tindart.net/.
Supplemental Material
- Vicente Dominguez, Pablo Messina, Ivania Donoso, and Denis Parra. 2019. The effect of explanations and algorithmic accuracy on visual recommender systems of artistic images. 408--416. https://doi.org/10.1145/3301275.3302274Google Scholar
- Sarah Frost and Manu Mathew Thomas. 2019. ART I DON'T LIKE: AN ANTI-RECOMMENDER SYSTEM FOR VISUAL ART.Google Scholar
- Ignacio Gatti. 2019. A Hybrid Approach for Artwork Recommendation. 281--284. https://doi.org/10.1109/AIKE.2019.00055Google Scholar
- Margaret Harris and George Butterworth. 2012. Developmental psychology: A student's handbook. Psychology Press.Google Scholar
- Pablo Messina, Vicente Dominguez, Denis Parra, Christoph Trattner, and Alvaro Soto. 2018. Content-based artwork recommendation: integrating painting metadata with neural and manually-engineered visual features. User Modeling and User-Adapted Interaction, Vol. 28 (07 2018), 40. https://doi.org/10.1007/s11257-018--9206--9Google Scholar
- Gjorgji Strezoski, Nanne van Noord, and Marcel Worring. 2019. Many task learning with task routing. In Proceedings of the IEEE International Conference on Computer Vision. 1375--1384.Google ScholarCross Ref
- Gjorgji Strezoski and Marcel Worring. 2018. Omniart: a large-scale artistic benchmark. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Vol. 14, 4 (2018), 1--21.Google ScholarDigital Library
Index Terms
- TindART: A Personal Visual Arts Recommender
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