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TindART: A Personal Visual Arts Recommender

Published: 12 October 2020 Publication History

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/.

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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 of which the decision making process is visualized through our web t-SNE implementation. TindART is live and available on desktop at: https://tindart.net/.

References

[1]
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/10.1145/3301275.3302274
[2]
Sarah Frost and Manu Mathew Thomas. 2019. ART I DON'T LIKE: AN ANTI-RECOMMENDER SYSTEM FOR VISUAL ART.
[3]
Ignacio Gatti. 2019. A Hybrid Approach for Artwork Recommendation. 281--284. https://doi.org/10.1109/AIKE.2019.00055
[4]
Margaret Harris and George Butterworth. 2012. Developmental psychology: A student's handbook. Psychology Press.
[5]
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--9
[6]
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.
[7]
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.

Cited By

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  • (2024)MultArtRec: A Multimodal Neural Topic Modeling for Integrating Image and Text Features in Artwork RecommendationElectronics10.3390/electronics1302030213:2(302)Online publication date: 10-Jan-2024
  • (2024)Artwork Recommendations based on User Preferences: Integrating Clustering Analysis with Visual FeaturesJournal on Computing and Cultural Heritage 10.1145/364990117:3(1-10)Online publication date: 15-May-2024
  • (2024)A hybrid approach for artwork recommendationEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.107173126:PDOnline publication date: 27-Feb-2024
  • Show More Cited By

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Published In

cover image ACM Conferences
MM '20: Proceedings of the 28th ACM International Conference on Multimedia
October 2020
4889 pages
ISBN:9781450379885
DOI:10.1145/3394171
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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New York, NY, United States

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Published: 12 October 2020

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  • NWO (Netherlands Organization for Scientific Research)

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Cited By

View all
  • (2024)MultArtRec: A Multimodal Neural Topic Modeling for Integrating Image and Text Features in Artwork RecommendationElectronics10.3390/electronics1302030213:2(302)Online publication date: 10-Jan-2024
  • (2024)Artwork Recommendations based on User Preferences: Integrating Clustering Analysis with Visual FeaturesJournal on Computing and Cultural Heritage 10.1145/364990117:3(1-10)Online publication date: 15-May-2024
  • (2024)A hybrid approach for artwork recommendationEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.107173126:PDOnline publication date: 27-Feb-2024
  • (2022)Towards a Construction Kit for Visual Recommender SystemsProceedings of the 2022 International Conference on Advanced Visual Interfaces10.1145/3531073.3534484(1-3)Online publication date: 6-Jun-2022
  • (2022)Towards Enhancing Virtual Museums by Contextualizing Art through Interactive VisualizationsJournal on Computing and Cultural Heritage 10.1145/352761915:4(1-26)Online publication date: 25-Jul-2022

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