RMSE

RMSE: Recommendation in

Multi-Stakeholder Environments

In conjunction with the 13th ACM Conference on Recommender Systems (RecSys2019)

Copenhagen, Denmark

The proceedings of the accepted papers are now online:

http://ceur-ws.org/Vol-2440/

Keynote speaker: Dr. Ed Chi

Title:

From Arrow’s Impossibility Theorem to Multi-Stakeholder, Multi-task, and More Inclusive Recommendations

Abstract:

As more researchers have become aware that recommendation systems actually have multiple stakeholders with differing objectives including fairness and inclusiveness constraints, there has been an explosion in works laying out new metrics, suggesting algorithms to address the issues, and calling attention to existing applications of machine learning fairness concepts in recommenders. This recent research has greatly expanded our understanding of the concerns and challenges in deploying machine learning in recommenders. In this talk, I will take us on a journey from thinking about Arrow’s impossibility theorem, which gives us the limits of designing a social welfare function that combines individual ranking preferences, to actual multi-task optimization approaches we’re taking in building real-world large-scale recommenders at Google.

Bio: Ed H. Chi is a Principal Scientist at Google, leading a machine learning research team focused on recommendation systems and social computing research. He has launched significant improvements of recommenders for YouTube, Google Play Store and Google+. With over 35 patents and over 100 research articles, he is known for research on Web and online social systems, and the effects of social signals on user behavior.

The workshop on Recommendation in Multi-Stakeholder Environments (RMSE) will be held in conjunction with the 13th ACM Conference on Recommender Systems in Copenhagen, Denmark.

One of the most essential aspects of any recommender system is personalization -- how well the recommendations delivered suit the user's interests. However, in many real world applications, there are other stakeholders whose needs and interests should be taken into account. In multisided e-commerce platforms, such as auction sites, there are parties on both sides of the recommendation transaction whose perspectives should be considered. There are also contexts in which the recommender system itself also has certain objectives that should be incorporated into the recommendation generation. Problems like long-tail promotion, fairness-aware recommendation, and profit maximization are all examples of objectives that may be important in different applications. In such multistakeholder environments, the recommender system will need to balance the (possibly conflicting) interests of different parties.

This workshop will encourage submissions that address the challenges of producing recommendations in multistakeholder settings, including but not limited to the following topics:

The requirements of different multistakeholder applications such as:
- Recommendation in multisided platforms
- Fairness-aware recommendation
- Multi-objective optimization in Recommendation
- Value-aware recommendation in commercial settings
- Reciprocal recommendation
Algorithms for multistakeholder recommendation including multi-objective optimization, re-ranking and others
Evaluation of multistakeholder recommendation systems
User experience considerations in multistakeholder recommendation including ethics, transparency, and interfaces for different stakeholders.