Recommender Systems are becoming an inherent part of today's Internet. They can be found anywhere from e-commerce platforms (eBay, Amazon) to music or movie streaming (Spotify, Netflix), social media (Facebook, Instagram, TikTok), travel platforms (Booking.com, Expedia), and much more. Whether a recommendation is successful or not can rely on multiple objectives such as user satisfaction, fairness issues, business value, and even societal issues. In addition, the long-term happiness (along with short-term excitements and delight) of the users is critical for a recommender system to be considered successful.
The objectives may also come from other stakeholders such as the item providers (those who provide the items to the platform to be recommended) or other stakeholders such as the platform owner or even side stakeholders such as society. For example, on a music streaming service, the platform may want to balance multiple objectives, some of which are related to the users and some related to the providers (artists) and even society as a whole. For instance, the platform may want to ensure a certain degree of exposure for different artists, so they reach their desired audience and to avoid monopoly by some superstars. The platform may also want to make sure it does not negatively affect the music culture of some smaller countries by over-exposing the users in those countries to some popular western music. These types of objectives and considerations exist in many other domains including social media, transportation, news recommendation, and food recommendation, to name a few.
Workshop main theme
MORS 2021 was a great success. Many interesting papers were presented and discussions were made. This year, we aim to organize MORS again to continue those discussions in the research community. In addition, we will have a main "theme" for this year's workshop: Long-term Optimization in Recommender Systems. While we accept any contribution related to multi-objective recommender systems, we highly encourage submissions related to long-term aspects of recommendations. We believe this aspect of recommendations has been largely overlooked in the research community, and it is crucial to pay more attention to this aspect. Long-term optimization refers to the fact that the recommendations given to the users should help achieve long-term satisfaction for the users rather than focusing only on the short-term metrics (e.g., precision, recall, etc.).
We encourage submissions that address the challenges related to having multiple objectives or multiple stakeholders in recommender systems. The topics of interest for the workshop include, but are not limited to:
Recommender systems with multiple objectives
Balancing the long-term impacts of the recommendations and the users' short-term preferences
Reinforcement Learning for long-term optimization in recommender systems
Feedback loops and the impact of recommendations in long term
Value-aware recommendation (profit, value, purpose, etc.)
The trade-off between relevance and bias in recommender systems
Recommendation with multiple stakeholders
Conflict handling in multi-stakeholder recommendation
Fairness-aware recommender systems