Multiple Objectives in Recommender Systems

Recommender systems are software tools that are used in a variety of application domains supporting users to find relevant items, products, and services easier. Historically, the main criterion for a successful recommender system was the relevance of the recommended items to the user. In other words, the only objective for the recommendation algorithm was to learn user's preferences for different items and generate recommendations accordingly. However, real-world recommender systems are well beyond a simple objective and often take into account multiple objectives. Indeed, different objectives can be important and should be considered for generating the recommendations. These objectives can be either from the users' perspective or they could come from other stakeholders such as item providers and the ones that could be impacted by the recommendations.

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 should 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 struggling is inevitable for the students to learn the new concepts properly. As a result, a problem recommender algorithm should balance between 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 the society. For example, on a music streaming service, the platform may want to balance multiple objectives some of which related to the users and some related to the providers (artists) and even the society as a whole. For instance, the platform may want to endure 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 small 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.

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

• Value-aware recommendation (profit, value, purpose, etc.)

• Trade-off between relevance and bias in recommender systems

• Recommendation with multiple stakeholders

• Food recommendation with different objectives

• Group recommender systems

• Conflict handling in multi-stakeholder recommendation

• Fairness-aware recommender systems

• Balancing the long-term impacts of the recommendations and the users' short term preferences

• News recommendation with editorial values

• Educational recommender systems with multiple, potentially conflicting, objectives

• Personalized medicine with the different objectives coming from the patients and physicians