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RecSys Challenge 2023: Deep Funnel Optimization with a Focus on User Privacy

Published:14 September 2023Publication History

ABSTRACT

The RecSys 2023 Challenge involved a conversion prediction task in the online advertising space. The dataset was provided by ShareChat (Mohalla Tech Pvt Ltd). The challenge data represents a sample of ad impressions served to the users over a period of 22 days and the task is for a given ad impression, to predict a conversion (install an app) will happen or not. The challenge ran for 3 months with a public dashboard. There were 519 teams registered and 231 teams made at least one submission. The task setting represents an important research area of modeling ad recommendations under user privacy. We identify interesting themes in feature engineering, addressing sparsity and calibrating across multi-step predictions.

References

  1. Fabian Abel, Yashar Deldjoo, Mehdi Elahi, and Daniel Kohlsdorf. 2017. RecSys Challenge 2017: Offline and Online Evaluation. In Proceedings of the Eleventh ACM Conference on Recommender Systems (Como, Italy) (RecSys ’17). Association for Computing Machinery, New York, NY, USA, 372–373. https://doi.org/10.1145/3109859.3109954Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Vito Walter Anelli, Amra Delić, Gabriele Sottocornola, Jessie Smith, Nazareno Andrade, Luca Belli, Michael Bronstein, Akshay Gupta, Sofia Ira Ktena, Alexandre Lung-Yut-Fong, Frank Portman, Alykhan Tejani, Yuanpu Xie, Xiao Zhu, and Wenzhe Shi. 2020. RecSys 2020 Challenge Workshop: Engagement Prediction on Twitter’s Home Timeline. In Proceedings of the 14th ACM Conference on Recommender Systems (Virtual Event, Brazil) (RecSys ’20). Association for Computing Machinery, New York, NY, USA, 623–627. https://doi.org/10.1145/3383313.3411532Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Vito Walter Anelli, Saikishore Kalloori, Bruce Ferwerda, Luca Belli, Alykhan Tejani, Frank Portman, Alexandre Lung-Yut-Fong, Ben Chamberlain, Yuanpu Xie, Jonathan Hunt, 2021. RecSys 2021 Challenge Workshop: Fairness-aware engagement prediction at scale on Twitter’s Home Timeline. In Proceedings of the 15th ACM Conference on Recommender Systems. 819–824.Google ScholarGoogle Scholar
  4. Dara Bahri, Heinrich Jiang, Yi Tay, and Donald Metzler. 2021. Scarf: Self-supervised contrastive learning using random feature corruption. arXiv preprint arXiv:2106.15147 (2021).Google ScholarGoogle Scholar
  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. In Proceedings of the Recommender Systems Challenge 2021 (Amsterdam, Netherlands) (RecSysChallenge ’21). Association for Computing Machinery, New York, NY, USA, 1–6. https://doi.org/10.1145/3487572.3487573Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Ching-Wei Chen, Paul Lamere, Markus Schedl, and Hamed Zamani. 2018. Recsys Challenge 2018: Automatic Music Playlist Continuation. In Proceedings of the 12th ACM Conference on Recommender Systems (Vancouver, British Columbia, Canada) (RecSys ’18). Association for Computing Machinery, New York, NY, USA, 527–528. https://doi.org/10.1145/3240323.3240342Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Yufei Feng, Fuyu Lv, Weichen Shen, Menghan Wang, Fei Sun, Yu Zhu, and Keping Yang. 2019. Deep session interest network for click-through rate prediction. arXiv preprint arXiv:1905.06482 (2019).Google ScholarGoogle Scholar
  8. Tianyu Gao, Xingcheng Yao, and Danqi Chen. 2021. Simcse: Simple contrastive learning of sentence embeddings. arXiv preprint arXiv:2104.08821 (2021).Google ScholarGoogle Scholar
  9. Peter Knees, Yashar Deldjoo, Farshad Bakhshandegan Moghaddam, Jens Adamczak, Gerard-Paul Leyson, and Philipp Monreal. 2019. RecSys Challenge 2019: Session-Based Hotel Recommendations. In Proceedings of the 13th ACM Conference on Recommender Systems (Copenhagen, Denmark) (RecSys ’19). Association for Computing Machinery, New York, NY, USA, 570–571. https://doi.org/10.1145/3298689.3346974Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Nick Landia, Rachael Mcalister, Donna North, Saikishore Kalloori, Abhishek Srivastava, and Bruce Ferwerda. 2022. RecSys Challenge 2022 Dataset: Dressipi 1M Fashion Sessions. In Proceedings of the Recommender Systems Challenge 2022 (Seattle, WA, USA) (RecSysChallenge ’22). Association for Computing Machinery, New York, NY, USA, 1–3. https://doi.org/10.1145/3556702.3556779Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Nick Landia, Rachael Mcalister, Donna North, Saikishore Kalloori, Abhishek Srivastava, and Bruce Ferwerda. 2022. RecSys Challenge 2022 Dataset: Dressipi 1M Fashion Sessions. In Proceedings of the Recommender Systems Challenge 2022. 1–3.Google ScholarGoogle Scholar
  12. Xiao Liu, Fanjin Zhang, Zhenyu Hou, Li Mian, Zhaoyu Wang, Jing Zhang, and Jie Tang. 2023. Self-Supervised Learning: Generative or Contrastive. IEEE Transactions on Knowledge and Data Engineering 35, 1 (2023), 857–876. https://doi.org/10.1109/TKDE.2021.3090866Google ScholarGoogle Scholar
  13. Pau Rodríguez, Miguel A Bautista, Jordi Gonzalez, and Sergio Escalera. 2018. Beyond one-hot encoding: Lower dimensional target embedding. Image and Vision Computing 75 (2018), 21–31.Google ScholarGoogle ScholarCross RefCross Ref
  14. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is All You Need. In Proceedings of the 31st International Conference on Neural Information Processing Systems (Long Beach, California, USA) (NIPS’17). Curran Associates Inc., Red Hook, NY, USA, 6000–6010.Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Fangye Wang, Yingxu Wang, Dongsheng Li, Hansu Gu, Tun Lu, Peng Zhang, and Ning Gu. 2023. CL4CTR: A Contrastive Learning Framework for CTR Prediction. In Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining. 805–813.Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Jinsung Yoon, Yao Zhang, James Jordon, and Mihaela van der Schaar. 2020. Vime: Extending the success of self-and semi-supervised learning to tabular domain. Advances in Neural Information Processing Systems 33 (2020), 11033–11043.Google ScholarGoogle Scholar
  17. Guorui Zhou, Na Mou, Ying Fan, Qi Pi, Weijie Bian, Chang Zhou, Xiaoqiang Zhu, and Kun Gai. 2019. Deep interest evolution network for click-through rate prediction. In Proceedings of the AAAI conference on artificial intelligence, Vol. 33. 5941–5948.Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Guorui Zhou, Xiaoqiang Zhu, Chenru Song, Ying Fan, Han Zhu, Xiao Ma, Yanghui Yan, Junqi Jin, Han Li, and Kun Gai. 2018. Deep interest network for click-through rate prediction. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining. 1059–1068.Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Xin Zhou, Aixin Sun, Yong Liu, Jie Zhang, and Chunyan Miao. 2023. Selfcf: A simple framework for self-supervised collaborative filtering. ACM Transactions on Recommender Systems 1, 2 (2023), 1–25.Google ScholarGoogle ScholarDigital LibraryDigital Library

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      cover image ACM Conferences
      RecSys '23: Proceedings of the 17th ACM Conference on Recommender Systems
      September 2023
      1406 pages

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      Publication History

      • Published: 14 September 2023

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      • extended-abstract
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      Overall Acceptance Rate254of1,295submissions,20%

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      18th ACM Conference on Recommender Systems
      October 14 - 18, 2024
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