Call for Papers

Submission deadline: Aug 1st, 2019 midnight UTC

Submit at: https://cmt3.research.microsoft.com/REVEAL2019

The site will start accepting submissions on June 1st.

State-of-the-art recommender systems are notoriously hard to design and improve upon, due to their interactive and dynamic nature, since they involve a multi-step decision-making process, where a stream of interactions occurs between the user and the system. Leveraging reward signals from these interactions and creating a scalable and performant recommendation inference model is a key challenge. Traditionally, to make the problem tractable, the interactions are often viewed as independent, but in order to improve recommender systems further, the models will need to take into account the delayed effects of each recommendation and start reasoning/planning for longer-term user satisfaction.

To this end, we welcome contributions that enable recommender systems to adapt effectively to diverse forms of user feedback and to optimize the quality of each user's long-term experience.

Potential contributions include (but are not limited to):

• Reinforcement learning and bandits for recommendation
• Robust estimators, counterfactual and off-policy evaluation
• Causal recommender systems
• Using simulation for recommender systems evaluation
• New evaluation datasets
• New offline metrics for recommender systems

Formatting instructions: Submissions are limited to a single extended abstract of at most two pages in RecSys format. Accepted submissions will be presented as talks and/or as posters.

Publication of submissions: If authors would like to provide longer versions of their submission, we will link to their papers on Arxiv. To be included on the workshop homepage, authors should send us the link to their Arxiv paper by September 15.

Reviewing process: The reviews will be single-blind.