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Publicity Chair RecSys21 <[log in to unmask]>
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Mon, 19 Apr 2021 14:57:07 +0000
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* We apologize if you receive multiple copies of this CfP *
* For the online version of this Call, visit: *
* PCS submissions are open here: *

We are pleased to invite you to contribute to the 15th ACM Conference on Recommender Systems (RecSys 2021), the premier venue for research on the foundations and applications of recommendation technologies. The upcoming RecSys conference will be held from September 27th to October 1st, 2021. The conference will be held in Amsterdam, Netherlands, with an inclusive format that accommodates remote attendance. The conference will continue RecSys’ practice of connecting the research and practitioner communities to exchange ideas, frame problems, and share solutions.

We invite proposals for tutorials to be given in conjunction with the conference. The goal of the tutorials is to provide conference attendees, including early-career researchers and researchers crossing-over from related disciplines, with an opportunity to learn about recommender system concepts and techniques. Tutorials also serve as a venue to share presenters’ expertise with the global community of recommender system researchers and practitioners. Tutorials focus on specific topics including, but not limited to:

*Introductions to specific recommender systems techniques (e.g., deep learning, feature engineering, tensorflow).
*Evaluation of recommender systems (e.g., system-centric and user-centric evaluation, experimentation).
*Context-aware (including location-based) recommender systems.
*Designing user experiences and interactions (e.g., virtual assistants, chatbots, etc.).
*Using different types of data (semantic web, graphs) and media (text, images, video, speech) for building recommendations.
*Ethical and legal aspects of recommender systems (e.g., privacy, fairness, accountability, transparency, and control of bias).
*Recommender systems facing real-world challenges (e.g., large-scale recommender systems or stream-based recommendation).
*Building and deploying recommender systems in specific domains (e.g., music, tourism, education, TV/video, jobs, enterprise, health, fashion).
*Recommender systems supporting decision making.
*Recommendation for groups, tasks, or situations, including intent-aware recommender systems.
*Eliciting and learning user preferences.
*Recommender systems that take users’ emotional state, physical state, personality, trust, level of expertise, and/or cognitive readiness into account.
*Sensors and recommender systems (including mobile recommender systems and wearables).
*Intersections of recommender systems with other domains (e.g., information retrieval, machine learning, human computer interaction, or databases).
*Recommender systems in new domains, such as e-government, smart cities and energy.

The length of your proposed tutorial should be commensurate with the presented materials and the projected interest of the RecSys community in the tutorial topic. We expect tutorial slots of either 90 or 180 minutes. We may work with accepted tutorial presenters to adjust the length of the tutorials for the available slots. Realize that you need to be flexible, since we may not be able to accommodate your favorite choice of date and time for the tutorial.

We actively encourage both researchers and industry practitioners to submit tutorial proposals that target different levels of expertise and different interests. We also encourage the submission of hands-on tutorials, for instance through the use of notebooks that combine theoretical concepts with practical exercises.

As a tutorial presenter, you are expected to write a short tutorial summary for the conference proceedings (detailed instructions will be provided), present your tutorial at the conference in person (possibly also streamed and, if you wish, recorded for the ACM RecSys 2021 YouTube channel), and provide a link to your tutorial materials after the tutorial so that it can be posted on the ACM RecSys 2021 website and serve as a resource to the community.

If you submit a tutorial, please realize that you are expected to attend the physical conference and present your tutorial in person. We are closely monitoring the COVID-19 situation. If, at the time of the conference, it is clear that travel is not possible for individual presenters, then we will make arrangements for you to be able to present remotely. However, we hope that the situation will improve and remote tutorial presentation will not be necessary.


All submissions and reviews will be handled electronically. Tutorial proposals must be submitted to PCS by 23:59, AoE (Anywhere on Earth) on 18 May 2021.

The tutorial proposal should be organized as follows:

*Tutorial title.
*Tutorial length.
*Motivation for proposing this tutorial (why it is important for RecSys).
*Name, email address, and affiliation of tutorial instructors (each listed instructor must present in person at the conference).
*Detailed bulleted outline of the tutorial (this point should take the most space).
*Targeted audience (introductory, intermediate, advanced) and prerequisite knowledge or skills.
*Teaching experiences and history of prior tutorials by the presenters.
*List of relevant publications by the presenters.

The submission should be a .pdf file of about 2 pages in length (single column, no particular formatting required).

Note that it is possible that we issue a second call for late-breaking tutorial proposals, but please do not count on it. We would appreciate it if you would submit your proposal to this call.


Tutorial proposals will be reviewed according to: ability of the tutorial to contribute to strengthening the foundations of recommender system research, or to broaden the field to look at important new challenges and techniques, experience and skill of the presenters, and the value of any materials released with the tutorial for the community.


*Tutorial proposals due: 18 May 2021
*Tutorial proposals notifications: 8 June 2021
*Camera ready tutorial session abstract: 14 June 2021


*Tat-Seng Chua, National University of Singapore, Singapore
*Alan Hanjalic, TU Delft, The Netherlands
*E-mail: [log in to unmask]

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