Workshop summary

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.

From the users' perspective, often multiple objectives need to be considered for generating the recommendations. For example, in restaurant recommendations, several factors, such as users' taste, diet restrictions, the proximity of the restaurant, and even the price could be taken into account. Each of these aspects can be important for the users, some of which, more than the others. Therefore, it is crucial for the recommender system to incorporate all these different objectives and aspects into account when recommending some restaurants to the user. Similarly, in the education domain, a student may prefer working on simpler problems to achieve higher scores. However, some struggle is inevitable for the students to learn the new concepts properly. As a result, a problem recommender algorithm should balance the simplicity of the recommended problems and their utility to help students learn more.

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.).

Workshop topics

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

Workshop Chairs:

• Himan Abdollahpouri (Spotify, USA)

• Shaghayegh Sahebi (University at Albany, State University of New York, USA)

• Mehdi Elahi (University of Bergen, Norway)

• Masoud Mansoury (University of Amsterdam, Netherlands)

• Babak Loni (Meta, Netherlands)

• Zahra Nazari (Spotify, USA)

• Maria Dimakopoulou (Spotify, USA)

MORS 2021 presentations