ACM Conference on Recommender Systems

Last updated
ACM Conference on Recommender Systems
AbbreviationRecSys
Discipline Recommender Systems
Publication details
Publisher ACM
History2007–present
FrequencyAnnual

ACM Conference on Recommender Systems (ACM RecSys) is a peer-reviewed academic conference series about recommender systems. Sponsored by the Association for Computing Machinery. 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.

Contents

The conference is sponsored by Big Tech companies such as Amazon, Netflix, Meta, Nvidia, Microsoft, Google, and Spotify, and large foundations such as the NSF. [1]

While an academic conference, RecSys attracts many practitioners and industry researchers, with industry attendance making up the majority of attendees, [2] this is also reflected in the authorship of research papers. [3] Many works published at the conference have direct impact on recommendation and personalization practice in industry [4] [5] [6] 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, [7] causal reasoning, [8] 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, [9] and Spotify. [10] Participation in the challenge is open to everyone and participation in it has become a means of showcasing ones skills in recommendations, [11] [12] similar to Kaggle competitions.

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. [13] [14] 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 Prize [15]

ByteDance Paper, 2022

In 2022, at one of the workshops at the conference, a paper from ByteDance, [16] 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. [17] [18] [19] [20]

List of conferences

Past and future RecSys conferences include:

YearLocationDateGeneral ChairsLink
2024 Bari, Italy October 14-18 Pasquale Lops, Tommaso Di Noia Website
2023 Singapore September 18-22 Jie Zhang, Li Chen, Shlomo Berkovsky Website
2022 Seattle, WA, USA and online September 18-23 Jen Golbeck, Max Harper, Vanessa Murdock Website
2021 Amsterdam, the Netherlands and online September 27 - October 1 Martha Larson, Martijn Willemsen, Humberto Corona Website
2020 Online September 22-26 Leandro Balby Marinho, Rodrygo Santos Website
2019 Copenhagen, Denmark September 16-20 Toine Bogers, Alan Said Website
2018 Vancouver, Canada October 2-7 Sole Pera, Michael Ekstrand Website
2017 Cernobbio, Italy August 27-31 Paolo Cremonesi, Francesco Ricci Website
2016 Boston, MA, USA September 15-19 Werner Geyer, Shilad Sen Website
2015 Vienna, Austria September 16-20 Hannes Werthner  [ de ], Markus Zanker Website
2014 Foster City, CA, USA October 6-10 Alfred Kobsa, Michelle Zhou Website
2013 Hong Kong, China October 12-16 Irwin King, Qiang Yang, Qing Li Website
2012 Dublin, Ireland September 9-13 Pádraig Cunningham, Neil Hurley Website
2011 Chicago, IL, USA October 23-27 Bamshad Mobasher, Robin Burke Website
2010 Barcelona, Spain September 26-30 Xavier Amatriain, Marc Torrens Website
2009 New York, NY, USA October 11-15 Lawrence Bergman, Alexander Tuzhilin Website
2008 Lausanne, Switzerland October 23-25 Pearl Pu Website
2007 Minneapolis, MN, USA September 19-20 Joe Konstan Website

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References

  1. ↑ "ACM RecSys 2022 Sponsorship" . Retrieved 2022-09-08.
  2. ↑ "RecSys 2020 Welcome Session". YouTube . Retrieved 2022-09-26.
  3. ↑ "TD Bank creates AI-powered Spotify playlist to win contest" . Retrieved 2022-09-26.
  4. ↑ "Wie entwickelt das ZDF Empfehlungsalgorithmen?" (in German). Retrieved 2022-09-26.
  5. ↑ "Διεθνής διάκριση ερευνητικής ομάδας του ΕΛΜΕΠΑ στο διαγωνισμό πληροφορικής του RecSys" (in Greek). Retrieved 2022-09-26.
  6. ↑ "Reverse Engineering The YouTube Algorithm: Part II" . Retrieved 2022-09-26.
  7. ↑ "The People Trying to Make Internet Recommendations Less Toxic" . Retrieved 2022-09-27.
  8. ↑ "New workshop to help bring causal reasoning to recommendation systems".
  9. ↑ "RecSys Challenge 2021" . Retrieved 2022-09-08.
  10. ↑ "RecSys Challenge 2018" . Retrieved 2022-09-08.
  11. ↑ "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.
  12. ↑ "TD's Layer 6 wins Spotify RecSys Challenge 2018" . Retrieved 2023-02-13.
  13. ↑ "BellKor's Pragmatic Chaos Wins $1 Million Netflix Prize by Mere Minutes" . Retrieved 2023-02-13.
  14. ↑ "How the Netflix Prize Was Won" . Retrieved 2023-02-13.
  15. ↑ "RecSys 2009 Program" . Retrieved 2023-02-13.
  16. ↑ 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].
  17. ↑ "#2 How TikTok Real Time Recommendation algorithm scales to billions?" . Retrieved 2023-02-13.
  18. ↑ "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.
  19. ↑ "Paper Review Monolith: Towards Better Recommendation Systems" . Retrieved 2023-02-13.
  20. ↑ "CHINA'S BYTEDANCE INTROS DIFFERENT APPROACH TO RECOMMENDATION AT SCALE" . Retrieved 2023-02-13.
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