ACM Conference on Recommender Systems (ACM RecSys) is an A-ranked[1] peer-reviewed academic conference series about recommender systems. It is held annually in different locations[2], and organized by different organizers, but a Steering Committee[3] supervises the organization. The conference proceedings are published by the Association for Computing Machinery[4]. Acceptance rates for full papers are typically below 20%.[5] 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.
| ACM Conference on Recommender Systems | |
|---|---|
| Abbreviation | RecSys |
| Discipline | Recommender Systems |
| Publication details | |
| Publisher | ACM |
| History | 2007–present |
| Frequency | Annual |
The conference is sponsored every year by ten to 20 Big Tech companies such as Amazon, Netflix, Meta, Nvidia, Microsoft, Google, and Spotify.[6]
While an academic conference, RecSys attracts many practitioners and industry researchers, with industry attendance making up the majority of attendees,[7] this is also reflected in the authorship of research papers.[8] Many works published at the conference have direct impact on recommendation and personalization practice in industry[9][10][11] 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,[12] causal reasoning,[13] 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,[14] and Spotify.[15] Participation in the challenge is open to everyone and participation in it has become a means of showcasing ones skills in recommendations,[16][17] similar to Kaggle competitions.
Notable Events
editNetflix Prize, 2009
editThe 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.[18][19] 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[20]
ByteDance Paper, 2022
editIn 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.[22][23][24][25]
List of conferences
editPast and future RecSys conferences include:
Key Numbers and History
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The ACM Recommender Systems Conference (RecSys) has experienced significant growth since its first event in 2007.[2][26] 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
edit- ^ "ACM RecSys Conference Ranking by CORE". portal.core.edu.au. Retrieved 2024-11-18.
- ^ a b "RecSys – ACM Recommender Systems". RecSys. Retrieved 2024-11-22.
- ^ "RecSys – ACM Recommender Systems". RecSys. Retrieved 2024-11-22.
- ^ "RECSYS Conference - Proceedings". ACM Digital Library. Retrieved 2024-11-22.
- ^ "RecSys – ACM Recommender Systems". RecSys. Retrieved 2024-11-18.
- ^ "2023 ACM RecSys Conference with the lowest number of industry sponsors since 2015 – RS_c". Retrieved 2024-11-18.
- ^ "RecSys 2020 Welcome Session". YouTube. Retrieved 2022-09-26.
- ^ "TD Bank creates AI-powered Spotify playlist to win contest". Retrieved 2022-09-26.
- ^ "Wie entwickelt das ZDF Empfehlungsalgorithmen?" (in German). Retrieved 2022-09-26.
- ^ "Διεθνής διάκριση ερευνητικής ομάδας του ΕΛΜΕΠΑ στο διαγωνισμό πληροφορικής του RecSys" (in Greek). Retrieved 2022-09-26.
- ^ "Reverse Engineering The YouTube Algorithm: Part II". Retrieved 2022-09-26.
- ^ "The People Trying to Make Internet Recommendations Less Toxic". Retrieved 2022-09-27.
- ^ "New workshop to help bring causal reasoning to recommendation systems".
- ^ "RecSys Challenge 2021". Retrieved 2022-09-08.
- ^ "RecSys Challenge 2018". Retrieved 2022-09-08.
- ^ "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.
- ^ "TD's Layer 6 wins Spotify RecSys Challenge 2018". Retrieved 2023-02-13.
- ^ "BellKor's Pragmatic Chaos Wins $1 Million Netflix Prize by Mere Minutes". Retrieved 2023-02-13.
- ^ "How the Netflix Prize Was Won". Retrieved 2023-02-13.
- ^ "RecSys 2009 Program". Retrieved 2023-02-13.
- ^ 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].
- ^ "#2 How TikTok Real Time Recommendation algorithm scales to billions?". Retrieved 2023-02-13.
- ^ "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.
- ^ "Paper Review Monolith: Towards Better Recommendation Systems". Retrieved 2023-02-13.
- ^ "CHINA'S BYTEDANCE INTROS DIFFERENT APPROACH TO RECOMMENDATION AT SCALE". Retrieved 2023-02-13.
- ^ "Stats on Submitted & Accepted Papers and Acceptance Rate at the ACM Recommender Systems Conference until 2023 – RS_c". Retrieved 2024-11-22.