extended-abstract

RecSys 2020 Challenge Workshop: Engagement Prediction on Twitter’s Home Timeline

Authors:

Vito Walter Anelli,

Gabriele Sottocornola,

Nazareno Andrade,

Michael Bronstein,

Sofia Ira Ktena,

Alexandre Lung-Yut-Fong,

Alykhan Tejani,

Wenzhe ShiAuthors Info & Claims

RecSys '20: Proceedings of the 14th ACM Conference on Recommender Systems

Pages 623 - 627

https://doi.org/10.1145/3383313.3411532

Published: 22 September 2020 Publication History

Abstract

The workshop features presentations of accepted contributions to the RecSys Challenge 2020, organized by Politecnico di Bari, Free University of Bozen-Bolzano, TU Wien, University of Colorado, Boulder, and Universidade Federal de Campina Grande, and sponsored by Twitter. The challenge focuses on a real-world task of Tweet engagement prediction in a dynamic environment. The goal is to predict the probability for different types of engagement (Like, Reply, Retweet, and Retweet with comment) of a target user for a set of Tweets, based on heterogeneous input data. To this end, Twitter has released a large public dataset of ~160M public Tweets, obtained by subsampling within ~2 weeks, that contains engagement features, user features, and Tweet features. A peculiarity of this challenge is related to the recent regulations on data protection and privacy. The challenge data set was compliant: if a user deleted a Tweet, or their data from Twitter, the dataset was promptly updated. Moreover, each change in the dataset implied new evaluations of all submissions and the update of the leaderboard metrics.

The challenge was well received with 1,131 registered users. In the final phase, 20 teams were competing for the winning position. These teams had an average size of approximately 4 participants and developed an overall number of 127 different methods.

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https://doi.org/10.1002/ejsc.12162
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
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cover image ACM Conferences

RecSys '20: Proceedings of the 14th ACM Conference on Recommender Systems

September 2020

796 pages

ISBN:9781450375832

DOI:10.1145/3383313

Copyright © 2020 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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Publication History

Published: 22 September 2020

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Extended-abstract
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RecSys '20

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RecSys '20: Fourteenth ACM Conference on Recommender Systems

September 22 - 26, 2020

Virtual Event, Brazil

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

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Cited By

Huijzer Rde Jonge PBlaauw FBaatenburg de Jong Mde Wit ADen Hartigh R(2024)Predicting special forces dropout via explainable machine learningEuropean Journal of Sport Science10.1002/ejsc.1216224:11(1564-1572)Online publication date: 24-Sep-2024
https://doi.org/10.1002/ejsc.12162
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
Agrawal RBrahme SMaitra SKalloori SSrivastava ALiu YIrissappane A(2023)RecSys Challenge 2023: Deep Funnel Optimization with a Focus on User PrivacyProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3610508(1217-1220)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3610508
Knyazev NOosterhuis H(2023)A Lightweight Method for Modeling Confidence in Recommendations with Learned Beta DistributionsProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608788(306-317)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3608788
Arazzi MCotogni MNocera AVirgili L(2023)Predicting Tweet Engagement with Graph Neural NetworksProceedings of the 2023 ACM International Conference on Multimedia Retrieval10.1145/3591106.3592294(172-180)Online publication date: 12-Jun-2023
https://dl.acm.org/doi/10.1145/3591106.3592294
Arrar DKamel NLakhfif A(2023)A comprehensive survey of link prediction methodsThe Journal of Supercomputing10.1007/s11227-023-05591-880:3(3902-3942)Online publication date: 7-Sep-2023
https://doi.org/10.1007/s11227-023-05591-8
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
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Landia NCheung FNorth DKalloori SSrivastava AFerwerda B(2022)RecSys Challenge 2022: Fashion Purchase PredictionProceedings of the 16th ACM Conference on Recommender Systems10.1145/3523227.3552534(694-697)Online publication date: 12-Sep-2022
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Toraman CŞahinuç FYilmaz EAkkaya I(2022)Understanding social engagements: A comparative analysis of user and text features in TwitterSocial Network Analysis and Mining10.1007/s13278-022-00872-112:1Online publication date: 31-Mar-2022
https://doi.org/10.1007/s13278-022-00872-1

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