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ACM Conference on Recommender Systems
Academic conference series

ACM Conference on Recommender Systems (ACM RecSys) is an A-ranked peer-reviewed academic conference series about recommender systems. It is held annually in different locations, and organized by different organizers, but a Steering Committee supervises the organization. The conference proceedings are published by the Association for Computing Machinery. Acceptance rates for full papers are typically below 20%. This conference series focuses on issues such as algorithms, machine learning, human-computer interaction, and data science from a multi-disciplinary perspective. The conference community includes computer scientists, statisticians, social scientists, psychologists, and others.

The conference is sponsored every year by ten to 20 Big Tech companies such as Amazon, Netflix, Meta, Nvidia, Microsoft, Google, and Spotify.

While an academic conference, RecSys attracts many practitioners and industry researchers, with industry attendance making up the majority of attendees, this is also reflected in the authorship of research papers. Many works published at the conference have direct impact on recommendation and personalization practice in industry affecting millions of users.

Recommender systems are pervasive in online systems, the conference provides opportunities for researchers and practitioners to address specific problems in various workshops in conjunction with the conference, topics include responsible recommendation, causal reasoning, and others. The workshop themes follow recent developments in the broader machine learning and human-computer interaction topics.

The conference is the host of the ACM RecSys Challenge, a yearly competition in the spirit of the Netflix Prize focussing on a specific recommendation problem. The Challenge has been organized by companies such as Twitter, and Spotify. Participation in the challenge is open to everyone and participation in it has become a means of showcasing ones skills in recommendations, similar to Kaggle competitions.

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Notable Events

Netflix Prize, 2009

The Netflix Prize was a recommendation challenge organized by Netflix between 2006 and 2009. Shortly prior to ACM RecSys 2009, the winners of the Netflix Prize were announced.1819 At the 2009 conference, members of the winning team (Bellkor's Pragmatich Chaos) as well as representatives from Netflix convened in a panel on the lessons learnt from the Netflix Prize20

ByteDance Paper, 2022

In 2022, at one of the workshops at the conference, a paper from ByteDance,21 the company behind TikTok, described in detail how a recommendation algorithm for video worked. While the paper did not point out the algorithm as the one that generates TikTok's recommendations, the paper received significant attention in technology-focused media.22232425

List of conferences

Past and future RecSys conferences include:

YearLocationDateGeneral ChairsLink
2025Prague, Czech RepublicSeptember 22-26Mária Bieliková, Pavel Kordik, Markus SchedlWebsite
2024Bari, ItalyOctober 14-18Pasquale Lops, Tommaso Di NoiaWebsite
2023SingaporeSeptember 18-22Jie Zhang, Li Chen, Shlomo BerkovskyWebsite
2022Seattle, WA, USA and onlineSeptember 18-23Jen Golbeck, Max Harper, Vanessa MurdockWebsite
2021Amsterdam, the Netherlands and onlineSeptember 27 - October 1Martha Larson, Martijn Willemsen, Humberto CoronaWebsite
2020OnlineSeptember 22-26Leandro Balby Marinho, Rodrygo SantosWebsite
2019Copenhagen, DenmarkSeptember 16-20Toine Bogers, Alan SaidWebsite
2018Vancouver, CanadaOctober 2-7Sole Pera, Michael EkstrandWebsite
2017 Cernobbio, ItalyAugust 27-31Paolo Cremonesi, Francesco RicciWebsite
2016Boston, MA, USASeptember 15-19Werner Geyer, Shilad SenWebsite
2015Vienna, AustriaSeptember 16-20Hannes Werthner [de], Markus ZankerWebsite
2014 Foster City, CA, USAOctober 6-10Alfred Kobsa, Michelle ZhouWebsite
2013Hong Kong, ChinaOctober 12-16Irwin King, Qiang Yang, Qing LiWebsite
2012Dublin, IrelandSeptember 9-13Pádraig Cunningham, Neil HurleyWebsite
2011 Chicago, IL, USAOctober 23-27Bamshad Mobasher, Robin BurkeWebsite
2010Barcelona, SpainSeptember 26-30Xavier Amatriain, Marc TorrensWebsite
2009New York, NY, USAOctober 11-15Lawrence Bergman, Alexander TuzhilinWebsite
2008Lausanne, SwitzerlandOctober 23-25Pearl PuWebsite
2007 Minneapolis, MN, USASeptember 19-20 Joe KonstanWebsite

Key Numbers and History

The ACM Recommender Systems Conference (RecSys) has experienced significant growth since its first event in 2007.2627 The number of paper submissions has steadily increased over the years. From an initial 35 submissions in 2007, the conference has seen over 250 submissions annually in recent years. While the number of submissions has increased, the conference's acceptance rate has become more selective, declining from 46% in its inaugural year to a range of 17-24% in more recent editions.

References

  1. "ACM RecSys Conference Ranking by CORE". portal.core.edu.au. Retrieved 2024-11-18. https://portal.core.edu.au/conf-ranks/28/ ↩

  2. "RecSys – ACM Recommender Systems". RecSys. Retrieved 2024-11-22. https://recsys.acm.org/# ↩

  3. "RecSys – ACM Recommender Systems". RecSys. Retrieved 2024-11-22. https://recsys.acm.org/steering-committee/ ↩

  4. "RECSYS Conference - Proceedings". ACM Digital Library. Retrieved 2024-11-22. https://dl.acm.org/conference/recsys/proceedings ↩

  5. "RecSys – ACM Recommender Systems". RecSys. Retrieved 2024-11-18. https://recsys.acm.org/statistics/ ↩

  6. "2023 ACM RecSys Conference with the lowest number of industry sponsors since 2015 – RS_c". Retrieved 2024-11-18. https://recommender-systems.com/news/2023/11/10/2023-acm-recsys-lowest-no-sponsors/ ↩

  7. "RecSys 2020 Welcome Session". YouTube. Retrieved 2022-09-26. https://www.youtube.com/watch?v=Xm0uUu3RZDM&t=184s ↩

  8. "TD Bank creates AI-powered Spotify playlist to win contest". Retrieved 2022-09-26. https://www.itworldcanada.com/article/td-bank-creates-ai-powered-spotify-playlist-to-win-contest/407604 ↩

  9. "Wie entwickelt das ZDF Empfehlungsalgorithmen?" (in German). Retrieved 2022-09-26. https://netzpolitik.org/2022/neues-aus-dem-fernsehrat-87-wie-entwickelt-das-zdf-empfehlungsalgorithmen/ ↩

  10. "Διεθνής διάκριση ερευνητικής ομάδας του ΕΛΜΕΠΑ στο διαγωνισμό πληροφορικής του RecSys" (in Greek). Retrieved 2022-09-26. https://www.anatolh.com/2022/09/01/diethnis-diakrisi-erevnitikis-omadas-tou-elmepa-sto-diagonismo-pliroforikis-tou-recsys/ ↩

  11. "Reverse Engineering The YouTube Algorithm: Part II". Retrieved 2022-09-26. https://www.tubefilter.com/2017/02/16/youtube-algorithm-reverse-engineering-part-ii/ ↩

  12. "The People Trying to Make Internet Recommendations Less Toxic". Retrieved 2022-09-27. https://www.wired.com/story/people-trying-make-internet-recommendations-less-toxic/ ↩

  13. "New workshop to help bring causal reasoning to recommendation systems". https://www.amazon.science/blog/new-workshop-to-help-bring-causal-reasoning-to-recommendation-systems ↩

  14. "RecSys Challenge 2021". Retrieved 2022-09-08. https://www.recsyschallenge.com/2021/ ↩

  15. "RecSys Challenge 2018". Retrieved 2022-09-08. https://www.recsyschallenge.com/2018/ ↩

  16. "Inside TD's AI play: How Layer 6's technology hopes to improve old-fashioned banking advice". The Globe and Mail. Retrieved 2022-09-27. https://www.theglobeandmail.com/business/article-inside-tds-ai-play-how-layer-6s-technology-hopes-to-improve-old/ ↩

  17. "TD's Layer 6 wins Spotify RecSys Challenge 2018". Retrieved 2023-02-13. https://www.newswire.ca/news-releases/tds-layer-6-wins-spotify-recsys-challenge-2018-689222261.html ↩

  18. "BellKor's Pragmatic Chaos Wins $1 Million Netflix Prize by Mere Minutes". Retrieved 2023-02-13. https://www.wired.com/2009/09/bellkors-pragmatic-chaos-wins-1-million-netflix-prize/ ↩

  19. "How the Netflix Prize Was Won". Retrieved 2023-02-13. https://www.wired.com/2009/09/how-the-netflix-prize-was-won/ ↩

  20. "RecSys 2009 Program". Retrieved 2023-02-13. https://recsys.acm.org/recsys09/program/ ↩

  21. Liu, Zhuoran; Zou, Leqi; Zou, Xuan; Wang, Caihua; Zhang, Biao; Tang, Da; Zhu, Bolin; Zhu, Yijie; Wu, Peng; Wang, Ke; Cheng, Youlong (2022). "Monolith: Real Time Recommendation System With Collisionless Embedding Table". arXiv:2209.07663 [cs.IR]. /wiki/ArXiv_(identifier) ↩

  22. "#2 How TikTok Real Time Recommendation algorithm scales to billions?". Retrieved 2023-02-13. https://www.machinelearningatscale.com/how-tiktok-recommendation-algorithm-scales-to-billion/ ↩

  23. "Computer Science Researchers at Bytedance Developed Monolith: a Collisionless Optimised Embedding Table for Deep Learning-Based Real-Time Recommendations in a Memory-Efficient Way". Retrieved 2023-02-13. https://www.marktechpost.com/2022/11/14/computer-science-researchers-at-bytedance-developed-monolith-a-collisionless-optimised-embedding-table-for-deep-learning-based-real-time-recommendations-in-a-memory-efficient-way/ ↩

  24. "Paper Review Monolith: Towards Better Recommendation Systems". Retrieved 2023-02-13. https://pub.towardsai.net/paper-review-monolith-towards-better-recommendation-systems-b58702be416a ↩

  25. "CHINA'S BYTEDANCE INTROS DIFFERENT APPROACH TO RECOMMENDATION AT SCALE". Retrieved 2023-02-13. https://www.nextplatform.com/2022/09/26/chinas-bytedance-intros-different-approach-to-recommendation-at-scale/ ↩

  26. "RecSys – ACM Recommender Systems". RecSys. Retrieved 2024-11-22. https://recsys.acm.org/# ↩

  27. "Stats on Submitted & Accepted Papers and Acceptance Rate at the ACM Recommender Systems Conference until 2023 – RS_c". Retrieved 2024-11-22. https://recommender-systems.com/news/2023/09/06/acm-recsys-stats-paper-submissions/ ↩