Call for Contributions
An increasing amount of research models recommender systems as a decision-making problem, instead of a pure prediction problem. Indeed, this view of the recommendation task makes it clear that recommendation decisions on real-world platforms have consequences.
These consequences come in many forms: algorithms are often responsible for collecting training data for the next iteration, but algorithmic decisions also impact the utility that all stakeholders can get from participating on the platform. Indeed, the recommender systems research community is well aware that solely optimising for short-term engagement metrics is not always in the best interests of all stakeholders (such as item providers or the platform itself), but the research literature that jointly deals with these problems remains sparse.
The CONSEQUENCES ‘22 workshop on Causality, Counterfactuals, and Sequential Decision-Making focused on these topics from a general point-of-view. In 2023, we wish to further this trend, whilst explicitly encouraging contributions that take into account multiple viewpoints and aim to understand, model and shape the consequences of recommendation decisions on all possible fronts. It is our belief that advances in the intersection of these fields foster progress in effective, efficient and fair learning and evaluation from logged data, and have a strong impact on practical systems.
The CONSEQUENCES workshop aims to bring a dedicated forum to learn and exchange ideas. To this end, we welcome contributions from both academia and industry and bring together a growing community of researchers and practitioners interested in sequential decision making, reinforcement learning, offline and off-policy evaluation, batch policy learning, fairness in online platforms, as well as other related tasks, such as A/B testing.
The CONSEQUENCES workshop is an in-person event co-located with the Seventeenth ACM Conference on Recommender Systems in Singapore. If necessary, arrangements will be made to allow speakers to present their accepted work remotely.
The workshop will accept contributions in the form of extended abstracts over three tracks:
Theoretical and Methodological Research
Benchmark, Datasets, and Software
Open Problems and Industry Experiences
List of Topics
Relevant topics to the workshop include, but are not limited to:
Multi-armed and Contextual Bandits, and other policy-learning problems for recommender systems
Learning, finding and designing metrics for long-term engagement and satisfaction
Benchmarks, datasets, and software
Causality in recommender systems
Counterfactual inference for learning and evaluation of recommender systems
Fairness and other long-term objectives in rankings and recommender systems
Open problems and challenges in applications
Important dates
Submission deadline August 3rd, 2023
Author notification August 27th, 2023
Camera-ready version deadline September 10th, 2023
CONSEQUENCES ‘23 September 19th, 2023
These dates consider in particular the synchronisation with the notification of acceptance in the main conference track, and the availability of early registration for the authors of accepted workshop papers.
Deadlines refer to 23:59 (11:59pm) in the AoE (Anywhere on Earth) time zone.
Submission instructions
Submit your contributions here: https://cmt3.research.microsoft.com/CONSEQUENCES2023/
Formatting instructions
Submissions are limited to a single extended abstract with a length of at most four pages (excluding references) in the new single-column RecSys template. Authors can optionally include an appendix with further technical details to support the reviewing procedure.
Reviewing process
Papers will be reviewed following a single-blind process. All accepted papers will be presented as posters, selected papers will be presented as long talks.
Publication process
Accepted contributions will be made publicly available on the workshop website as non-archival reports, allowing future submissions to archival conferences and journals.
Organisers
Olivier Jeunen, ShareChat
Thorsten Joachims , Information Science and Computer Science, Cornell University
Yuta Saito, Department of Computer Science, Cornell University
Harrie Oosterhuis, Radboud University
Flavian Vasile, Criteo
Yixin Wang, University of Michigan