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 an A-ranked [1] peer-reviewed academic conference series about recommender systems, published by the Association for Computing Machinery. Acceptance rates for full papers are typically below 20%. [2] 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 every year by ten to 20 Big Tech companies such as Amazon, Netflix, Meta, Nvidia, Microsoft, Google, and Spotify. [3]

While an academic conference, RecSys attracts many practitioners and industry researchers, with industry attendance making up the majority of attendees, [4] this is also reflected in the authorship of research papers. [5] Many works published at the conference have direct impact on recommendation and personalization practice in industry [6] [7] [8] 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, [9] causal reasoning, [10] 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, [11] and Spotify. [12] Participation in the challenge is open to everyone and participation in it has become a means of showcasing ones skills in recommendations, [13] [14] 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. [15] [16] 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 [17]

ByteDance Paper, 2022

In 2022, at one of the workshops at the conference, a paper from ByteDance, [18] 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. [19] [20] [21] [22]

List of conferences

Past and future RecSys conferences include:

YearLocationDateGeneral ChairsLink
2025 Prague, Czech Republic September 22-26 Mária Bieliková, Pavel Kordik, Markus Schedl Website
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

Related Research Articles

The Association for Computing Machinery (ACM) is a US-based international learned society for computing. It was founded in 1947 and is the world's largest scientific and educational computing society. The ACM is a non-profit professional membership group, reporting nearly 110,000 student and professional members as of 2022. Its headquarters are in New York City.

<span class="mw-page-title-main">Collaborative filtering</span> Algorithm

Collaborative filtering (CF) is, besides content-based filtering, one of two major techniques used by recommender systems. Collaborative filtering has two senses, a narrow one and a more general one.

A recommender system (RecSys), or a recommendation system (sometimes replacing system with terms such as platform, engine, or algorithm), is a subclass of information filtering system that provides suggestions for items that are most pertinent to a particular user. Recommender systems are particularly useful when an individual needs to choose an item from a potentially overwhelming number of items that a service may offer.

<span class="mw-page-title-main">Cuckoo hashing</span> Data structure hashing scheme

Cuckoo hashing is a scheme in computer programming for resolving hash collisions of values of hash functions in a table, with worst-case constant lookup time. The name derives from the behavior of some species of cuckoo, where the cuckoo chick pushes the other eggs or young out of the nest when it hatches in a variation of the behavior referred to as brood parasitism; analogously, inserting a new key into a cuckoo hashing table may push an older key to a different location in the table.

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SenSys, the ACM Conference on Embedded Networked Sensor Systems, is an annual academic conference in the area of embedded networked sensors.

IPSN, the IEEE/ACM International Conference on Information Processing in Sensor Networks, is an academic conference on sensor networks with its main focus on information processing aspects of sensor networks. IPSN draws upon many disciplines including signal and image processing, information and coding theory, networking and protocols, distributed algorithms, wireless communications, machine learning, embedded systems design, and databases and information management.

Cold start is a potential problem in computer-based information systems which involves a degree of automated data modelling. Specifically, it concerns the issue that the system cannot draw any inferences for users or items about which it has not yet gathered sufficient information.

The Netflix Prize was an open competition for the best collaborative filtering algorithm to predict user ratings for films, based on previous ratings without any other information about the users or films, i.e. without the users being identified except by numbers assigned for the contest.

<span class="mw-page-title-main">GroupLens Research</span> Computer science research lab

GroupLens Research is a human–computer interaction research lab in the Department of Computer Science and Engineering at the University of Minnesota, Twin Cities specializing in recommender systems and online communities. GroupLens also works with mobile and ubiquitous technologies, digital libraries, and local geographic information systems.

Music and artificial intelligence (AI) is the development of music software programs which use AI to generate music. As with applications in other fields, AI in music also simulates mental tasks. A prominent feature is the capability of an AI algorithm to learn based on past data, such as in computer accompaniment technology, wherein the AI is capable of listening to a human performer and performing accompaniment. Artificial intelligence also drives interactive composition technology, wherein a computer composes music in response to a live performance. There are other AI applications in music that cover not only music composition, production, and performance but also how music is marketed and consumed. Several music player programs have also been developed to use voice recognition and natural language processing technology for music voice control. Current research includes the application of AI in music composition, performance, theory and digital sound processing.

Gravity R&D is an IT vendor specialized in recommender systems. Gravity was founded by members of the Netflix Prize team "Gravity".

<span class="mw-page-title-main">Jen Golbeck</span> American computer scientist

Jennifer Golbeck is a computer scientist. She currently is a professor at the College of Information, an affiliate professor in the Computer Science Department, and an affiliate professor in the Journalism Department, all at the University of Maryland, College Park. Golbeck was director of the University of Maryland Human–Computer Interaction Lab from 2011 to 2014.

Social media mining is the process of obtaining data from user-generated content on social media in order to extract actionable patterns, form conclusions about users, and act upon the information. Mining supports targeting advertising to users or academic research. The term is an analogy to the process of mining for minerals. Mining companies sift through raw ore to find the valuable minerals; likewise, social media mining sifts through social media data in order to discern patterns and trends about matters such as social media usage, online behaviour, content sharing, connections between individuals, buying behaviour. These patterns and trends are of interest to companies, governments and not-for-profit organizations, as such organizations can use the analyses for tasks such as design strategies, introduce programs, products, processes or services.

<span class="mw-page-title-main">TikTok</span> Video-focused social media platform

TikTok, whose mainland Chinese and Hong Kong counterpart is Douyin, is a short-form video hosting service owned by Chinese internet company ByteDance. It hosts user-submitted videos, which can range in duration from three seconds to 60 minutes. It can be accessed with a smart phone app.

Matrix factorization is a class of collaborative filtering algorithms used in recommender systems. Matrix factorization algorithms work by decomposing the user-item interaction matrix into the product of two lower dimensionality rectangular matrices. This family of methods became widely known during the Netflix prize challenge due to its effectiveness as reported by Simon Funk in his 2006 blog post, where he shared his findings with the research community. The prediction results can be improved by assigning different regularization weights to the latent factors based on items' popularity and users' activeness.

Algorithmic radicalization is the concept that recommender algorithms on popular social media sites such as YouTube and Facebook drive users toward progressively more extreme content over time, leading to them developing radicalized extremist political views. Algorithms record user interactions, from likes/dislikes to amount of time spent on posts, to generate endless media aimed to keep users engaged. Through echo chamber channels, the consumer is driven to be more polarized through preferences in media and self-confirmation.

Mi Zhang is a computer scientist at Ohio State University, where he is an Associate Professor of Computer Science and Engineering and the director of AIoT and Machine Learning Systems Lab. He is best known for his work in Edge AI, Artificial Intelligence of Things (AIoT), machine learning systems, and mobile health.

TikTok Music is a music streaming app owned by ByteDance that was launched in July 2023. The Service allows users to listen to, download, and share songs. The service is set to close on November 28, 2024.

<i>Anderson v. TikTok</i> 2024 United States Third Circuit Court of Appeals case

Anderson v. TikTok, 2:22-cv-01849,, is a decision by the United States Court of Appeals for the Third Circuit in which the court held that Section 230 of the Communications Decency Act (CDA), 47 U.S.C. § 230, does not bar claims against TikTok, a video-sharing social media platform, regarding TikTok's recommendations to users via its algorithm.

References

  1. ↑ "ACM RecSys Conference Ranking by CORE". portal.core.edu.au. Retrieved 2024-11-18.
  2. ↑ "RecSys – ACM Recommender Systems". RecSys. Retrieved 2024-11-18.
  3. ↑ "2023 ACM RecSys Conference with the lowest number of industry sponsors since 2015 – RS_c" . Retrieved 2024-11-18.
  4. ↑ "RecSys 2020 Welcome Session". YouTube . Retrieved 2022-09-26.
  5. ↑ "TD Bank creates AI-powered Spotify playlist to win contest" . Retrieved 2022-09-26.
  6. ↑ "Wie entwickelt das ZDF Empfehlungsalgorithmen?" (in German). Retrieved 2022-09-26.
  7. ↑ "Διεθνής διάκριση ερευνητικής ομάδας του ΕΛΜΕΠΑ στο διαγωνισμό πληροφορικής του RecSys" (in Greek). Retrieved 2022-09-26.
  8. ↑ "Reverse Engineering The YouTube Algorithm: Part II" . Retrieved 2022-09-26.
  9. ↑ "The People Trying to Make Internet Recommendations Less Toxic" . Retrieved 2022-09-27.
  10. ↑ "New workshop to help bring causal reasoning to recommendation systems".
  11. ↑ "RecSys Challenge 2021" . Retrieved 2022-09-08.
  12. ↑ "RecSys Challenge 2018" . Retrieved 2022-09-08.
  13. ↑ "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.
  14. ↑ "TD's Layer 6 wins Spotify RecSys Challenge 2018" . Retrieved 2023-02-13.
  15. ↑ "BellKor's Pragmatic Chaos Wins $1 Million Netflix Prize by Mere Minutes" . Retrieved 2023-02-13.
  16. ↑ "How the Netflix Prize Was Won" . Retrieved 2023-02-13.
  17. ↑ "RecSys 2009 Program" . Retrieved 2023-02-13.
  18. ↑ 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].
  19. ↑ "#2 How TikTok Real Time Recommendation algorithm scales to billions?" . Retrieved 2023-02-13.
  20. ↑ "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.
  21. ↑ "Paper Review Monolith: Towards Better Recommendation Systems" . Retrieved 2023-02-13.
  22. ↑ "CHINA'S BYTEDANCE INTROS DIFFERENT APPROACH TO RECOMMENDATION AT SCALE" . Retrieved 2023-02-13.
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