We present TindART - a comprehensive visual arts recommender system. TindART leverages real time user input to build a usercentric 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/.
TindART: A Personal Visual Arts Recommender
Gjorgji Strezoski, Lucas Fijen, Jonathan Mitnik, Dániel László, Pieter de Marez Oyens, Yoni Schirris Marcel Worring
ACM International Conference on Multimedia (ACM MM), 2019 Demo
[Publication] [Web]
@inproceedings{10.1145/3394171.3414445,
author = {Strezoski, Gjorgji and Fijen, Lucas and Mitnik, Jonathan and L\'{a}szl\'{o}, D\'{a}niel and Oyens, Pieter de Marez and Schirris, Yoni and Worring, Marcel},
title = {TindART: A Personal Visual Arts Recommender},
year = {2020},
isbn = {9781450379885},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3394171.3414445},
doi = {10.1145/3394171.3414445},
booktitle = {Proceedings of the 28th ACM International Conference on Multimedia},
pages = {4524–4526},
numpages = {3},
location = {Seattle, WA, USA},
series = {MM '20}
}