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Learning Users’ Preferred Visual Styles in an Image Marketplace

Published: 13 September 2022 Publication History

Abstract

Providing meaningful recommendations in a content marketplace is challenging due to the fact that users are not the final content consumers. Instead, most users are creatives whose interests, linked to the projects they work on, change rapidly and abruptly. To address the challenging task of recommending images to content creators, we design a RecSys that learns visual styles preferences transversal to the semantics of the projects users work on. We analyze the challenges of the task compared to content-based recommendations driven by semantics, propose an evaluation setup, and explain its applications in a global image marketplace.

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MP4 File (acm_recsys_final.mp4)
Providing meaningful recommendations in a content marketplace is challenging due to the fact that users are not the final content consumers. Instead, most users are creatives whose interests, linked to the projects they work on, change rapidly and abruptly. To address the challenging task of recommending images to content creators, we design a RecSys that learns visual styles preferences transversal to the semantics of the projects users work on. We analyze the challenges of the task compared to content-based recommendations driven by semantics, propose an evaluation setup, and explain its applications in a global image marketplace.

References

[1]
Carlos A. Gomez-Uribe and Neil Hunt. 2016. The Netflix Recommender System: Algorithms and Business Value and and Innovation. ACM Transactions on Management Information Systems (2016).
[2]
Ruoxi Wang, Rakesh Shivanna, Derek Z. Cheng, Sagar Jain, Dong Lin, Lichan Hong, and Ed H. Chi. 2021. DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems. ACM World Wide Web Conference(2021).
[3]
Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shechtman, and Oliver Wang. 2018. The Unreasonable Effectiveness of Deep Features as a Perceptual Metric. Conference on Computer Vision and Pattern Recognition (2018).

Cited By

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  • (2023)CorrEmbed: Evaluating Pre-trained Model Image Similarity Efficacy with a Novel MetricArtificial Intelligence XL10.1007/978-3-031-47994-6_7(89-102)Online publication date: 12-Dec-2023

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RecSys '22: Proceedings of the 16th ACM Conference on Recommender Systems
September 2022
743 pages
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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 September 2022

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View all
  • (2023)CorrEmbed: Evaluating Pre-trained Model Image Similarity Efficacy with a Novel MetricArtificial Intelligence XL10.1007/978-3-031-47994-6_7(89-102)Online publication date: 12-Dec-2023

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