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RecSys Challenge 2022: Fashion Purchase Prediction

Authors:

Frederick Cheung,

Saikishore Kalloori,

Abhishek Srivastava,

Bruce FerwerdaAuthors Info & Claims

RecSys '22: Proceedings of the 16th ACM Conference on Recommender Systems

Pages 694 - 697

https://doi.org/10.1145/3523227.3552534

Published: 13 September 2022 Publication History

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Abstract

The RecSys 2022 Challenge was a session-based recommendation task in the fashion domain. The dataset was supplied by Dressipi. Given session data consisting of views and purchases, as well as content data representing the fashion characteristics of the items, the task was to predict which item was purchased at the end of the session. The challenge ran for 3 months with a public leaderboard and final result on a separate hidden test set. There were over 300 teams that submitted a solution to the leaderboard and about 50 that submitted a solution for the final test set. The winning team achieved a MRR score of 0.216 which means that the correct target item was on average ranked 5th in the list of predictions. We identify some interesting common themes among the solutions in this paper and the winning approaches are presented in the workshop.

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Cited By

Agrawal RBrahme SMaitra SSrivastava AIrissappane ALiu YKalloori S(2023)RecSys Challenge 2023 Dataset: Ads Recommendations in Online AdvertisingProceedings of the Recommender Systems Challenge 202310.1145/3626221.3627283(1-3)Online publication date: 19-Sep-2023
https://dl.acm.org/doi/10.1145/3626221.3627283
Ferrato A(2023)Challenges for Anonymous Session-Based Recommender Systems in Indoor EnvironmentsProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608879(1339-1341)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3608879
Landia NMcalister RNorth DKalloori SSrivastava AFerwerda B(2022)RecSys Challenge 2022 Dataset: Dressipi 1M Fashion SessionsProceedings of the Recommender Systems Challenge 202210.1145/3556702.3556779(1-3)Online publication date: 18-Sep-2022
https://dl.acm.org/doi/10.1145/3556702.3556779

Index Terms

RecSys Challenge 2022: Fashion Purchase Prediction
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Other conferences

RecSys '22: Proceedings of the 16th ACM Conference on Recommender Systems

September 2022

743 pages

ISBN:9781450392785

DOI:10.1145/3523227

Copyright © 2022 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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Association for Computing Machinery

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Publication History

Published: 13 September 2022

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RecSys '22

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RecSys '22: Sixteenth ACM Conference on Recommender Systems

September 18 - 23, 2022

WA, Seattle, USA

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

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Cited By

Agrawal RBrahme SMaitra SSrivastava AIrissappane ALiu YKalloori S(2023)RecSys Challenge 2023 Dataset: Ads Recommendations in Online AdvertisingProceedings of the Recommender Systems Challenge 202310.1145/3626221.3627283(1-3)Online publication date: 19-Sep-2023
https://dl.acm.org/doi/10.1145/3626221.3627283
Ferrato A(2023)Challenges for Anonymous Session-Based Recommender Systems in Indoor EnvironmentsProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608879(1339-1341)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3608879
Landia NMcalister RNorth DKalloori SSrivastava AFerwerda B(2022)RecSys Challenge 2022 Dataset: Dressipi 1M Fashion SessionsProceedings of the Recommender Systems Challenge 202210.1145/3556702.3556779(1-3)Online publication date: 18-Sep-2022
https://dl.acm.org/doi/10.1145/3556702.3556779

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