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Lightweight and Scalable Model for Tweet Engagements Predictions in a Resource-constrained Environment

Authors:

Luca Carminati,

Giacomo Lodigiani,

Pietro Maldini,

Arcangelo Pisa,

Alessandro Sanvito,

Mattia Surricchio,

Fernando Benjamín Pérez Maurera,

Cesare Bernardis,

Maurizio Ferrari DacremaAuthors Info & Claims

RecSysChallenge '21: Proceedings of the Recommender Systems Challenge 2021

Pages 28 - 33

https://doi.org/10.1145/3487572.3487597

Published: 22 November 2021 Publication History

Abstract

In this paper we provide an overview of the approach we used as team Trial&Error for the ACM RecSys Challenge 2021. The competition, organized by Twitter, addresses the problem of predicting different categories of user engagements (Like, Reply, Retweet and Retweet with Comment), given a dataset of previous interactions on the Twitter platform. Our proposed method relies on efficiently leveraging the massive amount of data, crafting a wide variety of features and designing a lightweight solution. This results in a significant reduction of computational resources requirements, both during the training and inference phase. The final model, an optimized LightGBM, allowed our team to reach the 4th position in the final leaderboard and to rank 1st among the academic teams.

References

[1]

Takuya Akiba, Shotaro Sano, Toshihiko Yanase, Takeru Ohta, and Masanori Koyama. 2019. Optuna: A Next-generation Hyperparameter Optimization Framework. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2019, Anchorage, AK, USA, August 4-8, 2019, Ankur Teredesai, Vipin Kumar, Ying Li, Rómer Rosales, Evimaria Terzi, and George Karypis(Eds.). ACM, 2623–2631. https://doi.org/10.1145/3292500.3330701

Digital Library

[2]

Vito Walter Anelli, Saikishore Kalloori, Bruce Ferwerda, Luca Belli, Alykhan Tejani, Frank Portman, Alexandre Lung-Yut-Fong, Ben Chamberlain, Yuanpu Xie, Jonathan Hunt, Michael M. Bronstein, and Wenzhe Shi. 2021. RecSys 2021 Challenge Workshop: Fairness-aware engagement prediction at scale on Twitter’s Home Timeline. In RecSys ’21: Fifteenth ACM Conference on Recommender Systems, Amsterdam, The Netherlands, 27 September 2021 - 1 October 2021, Humberto Jesús Corona Pampín, Martha A. Larson, Martijn C. Willemsen, Joseph A. Konstan, Julian J. McAuley, Jean Garcia-Gathright, Bouke Huurnink, and Even Oldridge (Eds.). ACM, 819–824. https://doi.org/10.1145/3460231.3478515

[3]

Sebastiano Antenucci, Simone Boglio, Emanuele Chioso, Ervin Dervishaj, Shuwen Kang, Tommaso Scarlatti, and Maurizio Ferrari Dacrema. 2018. Artist-driven layering and user’s behaviour impact on recommendations in a playlist continuation scenario. In Proceedings of the ACM Recommender Systems Challenge, RecSys Challenge 2018, Vancouver, BC, Canada, October 2, 2018. ACM, 4:1–4:6. https://doi.org/10.1145/3267471.3267475

Digital Library

[4]

Luca Belli, Sofia Ira Ktena, Alykhan Tejani, Alexandre Lung-Yut-Fong, Frank Portman, Xiao Zhu, Yuanpu Xie, Akshay Gupta, Michael M. Bronstein, Amra Delic, Gabriele Sottocornola, Vito Walter Anelli, Nazareno Andrade, Jessie Smith, and Wenzhe Shi. 2020. Privacy-Preserving Recommender Systems Challenge on Twitter’s Home Timeline. CoRR abs/2004.13715(2020). arxiv:2004.13715https://arxiv.org/abs/2004.13715

[5]

Luca Belli, Alykhan Tejani, Frank Portman, Alexandre Lung-Yut-Fong, Ben Chamberlain, Yuanpu Xie, Kristian Lum, Jonathan Hunt, Michael Bronstein, Vito Walter Anelli, Saikishore Kalloori, Bruce Ferwerda, and Wenzhe Shi. 2021. The 2021 RecSys Challenge Dataset: Fairness is not optional. arxiv:2109.08245 [cs.SI]

[6]

Tianqi Chen and Carlos Guestrin. 2016. XGBoost: A Scalable Tree Boosting System. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, August 13-17, 2016, Balaji Krishnapuram, Mohak Shah, Alexander J. Smola, Charu C. Aggarwal, Dou Shen, and Rajeev Rastogi (Eds.). ACM, 785–794. https://doi.org/10.1145/2939672.2939785

Digital Library

[7]

Dask Development Team. 2016. Dask: Library for dynamic task scheduling. https://dask.org

[8]

Gabriel de Souza Pereira Moreira, Sara Rabhi, Ronay Ak, Md Yasin Kabir, and Even Oldridge. 2021. Transformers with multi-modal features and post-fusion context for e-commerce session-based recommendation. CoRR abs/2107.05124(2021). arXiv:2107.05124https://arxiv.org/abs/2107.05124

[9]

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, June 2-7, 2019, Volume 1 (Long and Short Papers), Jill Burstein, Christy Doran, and Thamar Solorio (Eds.). Association for Computational Linguistics, 4171–4186. https://doi.org/10.18653/v1/n19-1423

[10]

Angela Fan, Yacine Jernite, Ethan Perez, David Grangier, Jason Weston, and Michael Auli. 2019. ELI5: Long Form Question Answering. CoRR abs/1907.09190(2019). arxiv:1907.09190http://arxiv.org/abs/1907.09190

[11]

Nicolò Felicioni, Andrea Donati, Luca Conterio, Luca Bartoccioni, Davide Yi Xian Hu, Cesare Bernardis, and Maurizio Ferrari Dacrema. 2020. Multi-Objective Blended Ensemble For Highly Imbalanced Sequence Aware Tweet Engagement Prediction. In RecSys Challenge ’20: Proceedings of the Recommender Systems Challenge 2020, Virtual Event Brazil, September, 2020. ACM, 29–33. https://dl.acm.org/doi/10.1145/3415959.3415998

Digital Library

[12]

Dietmar Jannach, Gabriel de Souza Pereira Moreira, and Even Oldridge. 2020. Why Are Deep Learning Models Not Consistently Winning Recommender Systems Competitions Yet?: A Position Paper. In RecSys Challenge ’20: Proceedings of the Recommender Systems Challenge 2020, Virtual Event Brazil, September, 2020. ACM, 44–49. https://dl.acm.org/doi/10.1145/3415959.3416001

Digital Library

[13]

Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. 2017. LightGBM: A Highly Efficient Gradient Boosting Decision Tree. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA, Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M. Wallach, Rob Fergus, S. V. N. Vishwanathan, and Roman Garnett (Eds.). 3146–3154. https://proceedings.neurips.cc/paper/2017/hash/6449f44a102fde848669bdd9eb6b76fa-Abstract.html

[14]

Daniele Micci-Barreca. 2001. A Preprocessing Scheme for High-Cardinality Categorical Attributes in Classification and Prediction Problems. SIGKDD Explor. 3, 1 (2001), 27–32. https://doi.org/10.1145/507533.507538

Digital Library

[15]

Jean-François Puget. 2019. Beyond Feature Engineering and HPO. https://www.youtube.com/watch?v=VC8Jc9_lNoY

[16]

Benedikt Schifferer, Gilberto Titericz, Chris Deotte, Christof Henkel, Kazuki Onodera, Jiwei Liu, Bojan Tunguz, Even Oldridge, Gabriel de Souza Pereira Moreira, and Ahmet Erdem. 2020. GPU Accelerated Feature Engineering and Training for Recommender Systems. In RecSys Challenge ’20: Proceedings of the Recommender Systems Challenge 2020, Virtual Event Brazil, September, 2020. ACM, 16–23. https://dl.acm.org/doi/10.1145/3415959.3415996

Digital Library

Cited By

Alari ACampana LCiliberto FMaggese SSgaravatti CZanella FPisani AFerrari Dacrema M(2024)Exploiting Contextual Normalizations and Article Endorsement for News RecommendationProceedings of the Recommender Systems Challenge 202410.1145/3687151.3687154(17-21)Online publication date: 14-Oct-2024
https://dl.acm.org/doi/10.1145/3687151.3687154
Basso PBenedetti ACecere NMaranelli AMarragony SPeri SRiboni AVerosimile AZanutto DFerrari Dacrema M(2023)Pessimistic Rescaling and Distribution Shift of Boosting Models for Impression-Aware Online Advertising RecommendationProceedings of the Recommender Systems Challenge 202310.1145/3626221.3627288(33-38)Online publication date: 19-Sep-2023
https://dl.acm.org/doi/10.1145/3626221.3627288
Maldini PSanvito ASurricchio M(2022)United We Stand, Divided We Fall: Leveraging Ensembles of Recommenders to Compete with Budget Constrained ResourcesProceedings of the Recommender Systems Challenge 202210.1145/3556702.3556845(34-38)Online publication date: 18-Sep-2022
https://dl.acm.org/doi/10.1145/3556702.3556845

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RecSysChallenge '21: Proceedings of the Recommender Systems Challenge 2021

October 2021

43 pages

ISBN:9781450386937

DOI:10.1145/3487572

Copyright © 2021 ACM.

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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New York, NY, United States

Publication History

Published: 22 November 2021

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RecSysChallenge 2021

RecSysChallenge 2021: Proceedings of the Recommender Systems Challenge 2021

October 1, 2021

Amsterdam, Netherlands

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Cited By

Alari ACampana LCiliberto FMaggese SSgaravatti CZanella FPisani AFerrari Dacrema M(2024)Exploiting Contextual Normalizations and Article Endorsement for News RecommendationProceedings of the Recommender Systems Challenge 202410.1145/3687151.3687154(17-21)Online publication date: 14-Oct-2024
https://dl.acm.org/doi/10.1145/3687151.3687154
Basso PBenedetti ACecere NMaranelli AMarragony SPeri SRiboni AVerosimile AZanutto DFerrari Dacrema M(2023)Pessimistic Rescaling and Distribution Shift of Boosting Models for Impression-Aware Online Advertising RecommendationProceedings of the Recommender Systems Challenge 202310.1145/3626221.3627288(33-38)Online publication date: 19-Sep-2023
https://dl.acm.org/doi/10.1145/3626221.3627288
Maldini PSanvito ASurricchio M(2022)United We Stand, Divided We Fall: Leveraging Ensembles of Recommenders to Compete with Budget Constrained ResourcesProceedings of the Recommender Systems Challenge 202210.1145/3556702.3556845(34-38)Online publication date: 18-Sep-2022
https://dl.acm.org/doi/10.1145/3556702.3556845

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