CONSEQUENCES+REVEAL '22

The CONSEQUENCES+REVEAL Workshop is now a merged event instead of independent workshops.
Organisers are working together to implement a new 2-day workshop format, where the programme will be grouped over two days based on the topic.

Call for Contributions

Recommender systems are increasingly modelled as repeated decision making processes that decide which items to recommend to a given user. Each decision to recommend an item or slate of items has a significant impact on immediate and future user responses, long-term satisfaction or engagement with the system, and possibly valuable exposure for the item provider. This interactive and interventionist view of recommendation uncovers a plethora of unanswered research questions, as it complicates the classical offline evaluation and learning procedures in the field.

A first challenge is to develop a deep understanding of causal inference to reason about (possibly unintended) consequences of the recommender, and a notion of counterfactuals to answer common “what if”- type questions in learning and evaluation. Advances in the intersection of these fields can foster progress in effective, efficient and fair learning and evaluation from logged data. This will be the focus of the CONSEQUENCES workshop.

A second challenge is to optimise a multi-step decision-making process, where a stream of interactions occurs between the user and the system. Deriving reward signals from these interactions, and creating a scalable, performant, and maintainable recommendation model to use for inference is a key challenge for machine learning teams, both in industry and academia. To make the system design a bit more tractable, these environment interactions are often viewed as independent; but to further improve and scale recommender systems, the models must take into account the delayed effects of each recommendation, and begin reasoning/planning for longer-term user satisfaction, leveraging techniques such as Reinforcement Learning (RL). This will be the focus of the REVEAL workshop.

These topics have been emerging in the Recommender Systems community for a while, our 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+REVEAL workshop is an in-person event co-located with the Sixteenth ACM Conference on Recommender Systems in Seattle, WA, USA. 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:

1. Theoretical and Methodological Research

2. Benchmark, Datasets, and Software

3. Open Problems and Industry Experiences

Authors do not have to specify to which workshops they submit their paper. All papers are expected to be presented at several sessions (poster or oral) implemented across the CONSEQUENCES and the REVEAL workshop days, based on the topic.

List of Topics

Relevant topics to the workshop include, but are not limited to:

• Reinforcement Learning, Bandits and other policy-learning problems for recommender systems

• 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 5th, 2022

Author notification August 27th, 2022

Camera-ready version deadline September 10th, 2022

REVEAL ‘22 September 22nd 2022 (Room: Ballroom CD)

CONSEQUENCES ‘22 September 23rd, 2022 (Room: Ballroom CD)

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 at: https://cmt3.research.microsoft.com/CONSEQUENCES2022

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

CONSEQUENCES

• Olivier Jeunen, Amazon

• Thorsten Joachims , Information Science and Computer Science, Cornell University

• Yuta Saito, Department of Computer Science, Cornell University

• Harrie Oosterhuis, Radboud University and Twitter

• Flavian Vasile, Criteo

REVEAL

• Paige Bailey, Google

• Maria Dimakopoulou, Spotify

• Ying Li, Netflix

• Richard Liaw, Anyscale

• Justin Basilico, Netflix

• Yves Raimond, Netflix