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Flavio Figueiredo <[log in to unmask]>
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Mon, 17 Aug 2020 15:27:31 +0000
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The ACM Recommender Systems conference (RecSys) is the premier international forum for the presentation of new research results, systems and techniques in the broad field of recommender systems. We are pleased to invite you to participate in RecSys 2020, which will be held entirely online during September 22-26, 2020.


* Early-bird registration: August 21, 2020
* Main conference: September 22-24, 2020
* Tutorials & workshops: September 25-26, 2020


Registration is open at Register on or before August 21 to benefit from an early-bird discount!


RecSys 2020 will be held entirely online. In transitioning online, our goal is to offer attendees an experience as close as possible to what makes RecSys great, while making the necessary adjustments to make it inclusive for our international community around the globe. In particular, the main conference will keep its traditional single-track structure, beginning with a keynote each day followed by multiple paper sessions and a poster/demo session. Longer breaks between sessions will allow attendees to network with other attendees and sponsors, engage in several virtual social activities, or simply chill out before the next session. Instead of choosing one particular timezone, the main conference will follow a timezone-agnostic, always-on schedule. In practice, attendees should be able to watch and interact live with all the content presented through September 22-24, 2020. Tutorials and workshops on September 25-26, 2020 will offer a mix of synchronous and asynchronous activities. For further details, please visit:


RecSys 2020 will bring together researchers and practitioners from academia and industry to present their latest results and identify new trends and challenges in providing recommendation components in a range of innovative application contexts. In addition to the main technical track, the RecSys 2020 program will feature keynote talks, tutorials covering state-of-the-art in this domain, a workshop program, an industrial track and a doctoral symposium.


* Bias on Search and Recommender Systems
by Ricardo Baeza-Yates (Universidad de Chile & Northeastern University, USA)

* 4 Reasons Why Social Media Make Us Vulnerable to Manipulation
by Filippo Menczer (Indiana University, USA)

* “You Really Get Me”: Conversational AI Agents That Can Truly Understand and Help Users
by Michelle Zhou (Juji, Inc.)


* Long papers:
* Reproducibility papers:
* Industry talks:


* Short papers:
* Demonstrations:
* Late-breaking results:
* Doctoral symposium:


* Feature Engineering for Recommender Systems
by Benedikt Schifferer (Nvidia), Chris Deotte (Nvidia) and Even Oldridge (Nvidia)

* Bayesian Value Based Recommendation: A Modelling based Alternative to Proxy and Counterfactual Policy based Recommendation
by David Rohde (Criteo), Flavian Vasile (Criteo), Sergey Ivanov (Criteo), and Otmane Sakhi (Criteo)

* Counteracting Bias and Increasing Fairness in Search and Recommender Systems
by Ruoyuan Gao (Rutgers University) and Chirag Shah (University of Washington)

* Adversarial Learning for Recommendation: Applications for Security and Generative Tasks – Concept to Code
by Vito Walter Anelli (Polytechnic University of Bari), Yashar Deldjoo (Polytechnic University of Bari), Tommaso Di Noia (Polytechnic University of Bari) and Felice Antonio Merra (Polytechnic University of Bari)

* Introduction to Bandits in Recommender Systems
by Andrea Barraza-Urbina (NUI Galway) and Dorota Glowacka (University of Helsinki)

* Tutorial on Conversational Recommender Systems
by Yongfeng Zhang (Rutgers University), Zuohui Fu (Rutgers University), Yikun Xian (Rutgers University), and Yi Zhang (University of California Santa Cruz)


* CARS: Workshop on Context-Aware Recommender Systems

* ComplexRec: Workshop on Recommendation in Complex Environments

* FAccTRec: Workshop on Responsible Recommendation

* fashionXrecsys: Workshop on Recommender Systems in Fashion and Retail

* HealthRecSys: Workshop on Health Recommender Systems

* ImpactRS: Workshop on the Impact of Recommender Systems

* IntRS: Joint Workshop on Interfaces and Human Decision Making for Recommender Systems

* OHARS: Workshop on Online Misinformation- and Harm-Aware Recommender Systems

* ORSUM: Workshop on Online Recommender Systems and User Modeling

* PodRecs: Workshop on Podcast Recommendations

* REVEAL: Workshop on Bandit and Reinforcement Learning from User Interactions

* RecSys Challenge 2020 Workshop


* Rodrygo Santos, Universidade Federal de Minas Gerais, Brazil
* Leandro Marinho, Universidade Federal de Campina Grande, Brazil


* Elizabeth M. Daly, IBM Research, Ireland
* Li Chen, Hong Kong Baptist University, Hong Kong, China

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