Dynamic Product Image Generation and Recommendation at Scale for Personalized E-commerce
Pages 768 - 770
Abstract
Coupling latent diffusion based image generation with contextual bandits enables the creation of eye-catching personalized product images at scale that was previously either impossible or too expensive. In this paper we showcase how we utilized these technologies to increase user engagement with recommendations in online retargeting campaigns for e-commerce.
References
[1]
Fernando Amat, Ashok Chandrashekar, Tony Jebara, and Justin Basilico. 2018. Artwork personalization at netflix. In Proceedings of the 12th ACM Conference on Recommender Systems (Vancouver, British Columbia, Canada) (RecSys ’18). Association for Computing Machinery, New York, NY, USA, 487–488. https://doi.org/10.1145/3240323.3241729
[2]
Wei Chu, Lihong Li, Lev Reyzin, and Robert Schapire. 2011. Contextual Bandits with Linear Payoff Functions. In Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics(Proceedings of Machine Learning Research, Vol. 15), Geoffrey Gordon, David Dunson, and Miroslav Dudík (Eds.). PMLR, Fort Lauderdale, FL, USA, 208–214. https://proceedings.mlr.press/v15/chu11a.html
[3]
Anjan Goswami, Naren Chittar, and Chung H Sung. 2011. A study on the impact of product images on user clicks for online shopping. In Proceedings of the 20th international conference companion on World wide web. 45–46.
[4]
Lihong Li, Wei Chu, John Langford, and Robert E. Schapire. 2010. A contextual-bandit approach to personalized news article recommendation. In Proceedings of the 19th international conference on World wide web(WWW ’10). ACM. https://doi.org/10.1145/1772690.1772758
[5]
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. 2022. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 10684–10695.
[6]
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. 2021. Score-Based Generative Modeling through Stochastic Differential Equations. (2021).
[7]
Patrick von Platen, Suraj Patil, Anton Lozhkov, Pedro Cuenca, Nathan Lambert, Kashif Rasul, Mishig Davaadorj, Dhruv Nair, Sayak Paul, William Berman, Yiyi Xu, Steven Liu, and Thomas Wolf. 2022. Diffusers: State-of-the-art diffusion models. https://github.com/huggingface/diffusers.
[8]
Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. 2023. Adding conditional control to text-to-image diffusion models. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 3836–3847.
Index Terms
- Dynamic Product Image Generation and Recommendation at Scale for Personalized E-commerce
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Published In
October 2024
1438 pages
ISBN:9798400705052
DOI:10.1145/3640457
Copyright © 2024 Owner/Author.
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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- SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web
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- SIGIR: ACM Special Interest Group on Information Retrieval
- SIGCHI: ACM Special Interest Group on Computer-Human Interaction
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Association for Computing Machinery
New York, NY, United States
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Published: 08 October 2024
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