ACM Conference on Recommender Systems
| ACM Conference on Recommender Systems | |
|---|---|
| Abbreviation | RecSys |
| Discipline | Recommender Systems |
| Publication details | |
| Publisher | ACM |
| History | 2007–present |
| Frequency | Annual |
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.
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[edit]
Netflix Prize, 2009[edit]
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[edit]
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[edit]
Past and future RecSys conferences include:
References[edit]
- ^ "ACM RecSys 2022 Sponsorship". Retrieved 2022-09-08.
- ^ "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.