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A hierarchical bayesian model for size recommendation in fashion

Authors:

Reza ShirvanyAuthors Info & Claims

RecSys '18: Proceedings of the 12th ACM Conference on Recommender Systems

Pages 392 - 396

https://doi.org/10.1145/3240323.3240388

Published: 27 September 2018 Publication History

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Abstract

We introduce a hierarchical Bayesian approach to tackle the challenging problem of size recommendation in e-commerce fashion. Our approach jointly models a size purchased by a customer, and its possible return event: 1. no return, 2. returned too small 3. returned too big. Those events are drawn following a multinomial distribution parameterized on the joint probability of each event, built following a hierarchy combining priors. Such a model allows us to incorporate extended domain expertise and article characteristics as prior knowledge, which in turn makes it possible for the underlying parameters to emerge thanks to sufficient data. Experiments are presented on real (anonymized) data from millions of customers along with a detailed discussion on the efficiency of such an approach within a large scale production system.

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Cited By

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Ding YLai ZMok PChua T(2023)Computational Technologies for Fashion Recommendation: A SurveyACM Computing Surveys10.1145/362710056:5(1-45)Online publication date: 25-Nov-2023
https://dl.acm.org/doi/10.1145/3627100
Deldjoo YNazary FRamisa AMcAuley JPellegrini GBellogin ANoia T(2023)A Review of Modern Fashion Recommender SystemsACM Computing Surveys10.1145/362473356:4(1-37)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3624733
Puzikov YPecenakova SWeffer RLefakis LShirvany R(2023)Identification of Fine-Grained Fit Information from Customer Reviews in FashionRecommender Systems in Fashion and Retail10.1007/978-3-031-22192-7_1(1-23)Online publication date: 2-Mar-2023
https://doi.org/10.1007/978-3-031-22192-7_1
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Published In

RecSys '18: Proceedings of the 12th ACM Conference on Recommender Systems

September 2018

600 pages

ISBN:9781450359016

DOI:10.1145/3240323

General Chairs:
Sole Pera
Boise State University
,
Michael Ekstrand
Boise State University
,
Program Chairs:
Xavier Amatriain
Curai, USA
,
John O'Donovan
University of California, Santa Barbara

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 27 September 2018

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SIGCHI

RecSys '18: Twelfth ACM Conference on Recommender Systems

October 2, 2018

British Columbia, Vancouver, Canada

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RecSys '18 Paper Acceptance Rate 32 of 181 submissions, 18%;

Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Ding YLai ZMok PChua T(2023)Computational Technologies for Fashion Recommendation: A SurveyACM Computing Surveys10.1145/362710056:5(1-45)Online publication date: 25-Nov-2023
https://dl.acm.org/doi/10.1145/3627100
Deldjoo YNazary FRamisa AMcAuley JPellegrini GBellogin ANoia T(2023)A Review of Modern Fashion Recommender SystemsACM Computing Surveys10.1145/362473356:4(1-37)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3624733
Puzikov YPecenakova SWeffer RLefakis LShirvany R(2023)Identification of Fine-Grained Fit Information from Customer Reviews in FashionRecommender Systems in Fashion and Retail10.1007/978-3-031-22192-7_1(1-23)Online publication date: 2-Mar-2023
https://doi.org/10.1007/978-3-031-22192-7_1
Varsha VDevarasetty SS SS R PR J(2022)Personalised Fashion AssistantProceedings of the 2022 4th Asia Pacific Information Technology Conference10.1145/3512353.3512364(72-80)Online publication date: 14-Jan-2022
https://dl.acm.org/doi/10.1145/3512353.3512364
Pecenakova SKaressli NShirvany R(2022)FitGAN: Fit- and Shape-Realistic Generative Adversarial Networks for Fashion2022 26th International Conference on Pattern Recognition (ICPR)10.1109/ICPR56361.2022.9956089(3097-3104)Online publication date: 21-Aug-2022
https://doi.org/10.1109/ICPR56361.2022.9956089
Zafar HLasserre JShirvany R(2022)SkillSF: In the Sizing Game, Your Size is Your SkillRecommender Systems in Fashion and Retail10.1007/978-3-030-94016-4_4(49-61)Online publication date: 8-Mar-2022
https://doi.org/10.1007/978-3-030-94016-4_4
Bahuleyan HLasserre JLefakis LShirvany R(2022)Knowing When You Don’t Know in Online Fashion: An Uncertainty-Aware Size Recommendation FrameworkRecommender Systems in Fashion and Retail10.1007/978-3-030-94016-4_3(33-48)Online publication date: 8-Mar-2022
https://doi.org/10.1007/978-3-030-94016-4_3
Mohammadi SKalhor A(2021)Smart Fashion: A Review of AI Applications in Virtual Try-On & Fashion SynthesisJournal of Artificial Intelligence and Capsule Networks10.36548/jaicn.2021.4.0023:4(284-304)Online publication date: 26-Nov-2021
https://doi.org/10.36548/jaicn.2021.4.002
Chakraborty SHoque MRahman Jeem NBiswas MBardhan DLobaton E(2021)Fashion Recommendation Systems, Models and Methods: A ReviewInformatics10.3390/informatics80300498:3(49)Online publication date: 26-Jul-2021
https://doi.org/10.3390/informatics8030049
Jaradat SDokoohaki NCorona Pampin HShirvany R(2021)Workshop on Recommender Systems in Fashion and RetailProceedings of the 15th ACM Conference on Recommender Systems10.1145/3460231.3470926(810-812)Online publication date: 13-Sep-2021
https://dl.acm.org/doi/10.1145/3460231.3470926
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SizeFlags: Reducing Size and Fit Related Returns in Fashion E-Commerce

Brand competition in fashion e-commerce

BOXREC: Recommending a Box of Preferred Outfits in Online Shopping

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