Nowadays, recommender systems are employed across a diverse set of application domains, not only supporting us in our decision making and choices but also helping us to discover and find new items, products, and services much more efficiently. The commonly used approach in recommender systems is receiver-centric (or user-centric) where the focus is on satisfying the receiver of the recommendations without any considerations for business \& strategic objectives or the objectives of the item providers.


A recommender system that does not include strategic or business-related objectives is often referred to as "organic" recommendations, emphasizing personalized recommendations with exclusive consideration for user relevance. Conversely, strategic, sponsored or utility-aware recommendations adopt a different perspective, with the objective to optimize for both user relevancy and some kind of utility associated with those recommendations. In contrast to an organic recommender system, the focus of a strategic (or so called “non-organic”) recommender system is to identify the most “relevant” users for a given item to maximize utility.


Utility can be defined in many ways depending on the problem we are solving and the domain on which the recommender systems in operating. For example, for a job recommender system on Linkedin, the utility could be to ensure the person who receive a certain job recommendation actually is qualified for it and fits the criteria of the recruiter who listed the job. It could also be monetary where a one recommendation might have a higher profit margin than another when they may have similar relevance from the user's perspective.


In real-world applications of recommender systems, aligning user-centric recommendation with overarching strategies supporting creators in their growth has become imperative in many multi-sided platforms. With advanced development of technology and research in the domain, the pressing need for a closer integration of recommender systems with both long and short term strategic goals becomes more clear. This introduces various challenges that are worth further investigation by the research community and industry practitioners.


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We encourage submissions that address the challenges related to situations where there are some kind of utility involved with the recommendations and/or where there are multiple objectives or multiple stakeholders in recommender systems. The topics of interest for the workshop include, but are not limited to:


- Recommendation with multiple stakeholders

- Applications of personalization in advertising and promotions

- Recommender systems with multiple objectives

- Studying different domains where strategic and utility-aware recommendations can be important. For instance, in job recommendation, providers (recruiter) may have preference for who should receive their recommendations but that may not be the case in a typical movie recommendation.

- Estimation and optimization methods for the long-term value of recommender systems

- Long-term community or audience growth for recommended items

- Explainable recommendations, especially for strategic and utility-aware recommendations.

- Methods for estimating trade-offs between user retention and satisfaction and a utility value of recommendations

- The impact of different objectives on the short-term and long-term success of the recommender systems.

- Simulation for experimentation in multi-objective, multi-stakeholder recommenders, and long-term user satisfaction.

- Users’ trust in recommendations for non-organic recommendations.

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Workshop Chairs:
Himan Abdollahpouri (Spotify, USA)
Tonia Danylenko (Spotify, Sweden)
Masoud Mansoury (TU Delft, Netherlands)
Babak Loni (Meta, Netherlands)
Daniel Russo (Columbia Business School, USA)
Mihajlo Grbovic (Airbnb, USA)

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Important dates:
Paper submission deadline: August 5, 2024
Author notification: August 27, 2024
Camera-ready version deadline: September 5, 2024