Research in the area of recommender systems is largely focused on helping individual users finding items they are interested in.

Recommender systems can have broad directly-measurable impacts, e.g., such that go beyond the individual user or the short term influence. A recommender system on a news platform, for example, can lead to a shift in the reading patterns of the entire user base. On the other hand, recommender systems usually serve certain business goals and can have an impact not only on the customers, e.g., by stimulating higher engagement on a media streaming platform or a social network, but also direct and indirect affect sales, revenue or conversion and churn rates.

The topics of interest include, e.g.,
* Field studies on the impact and business value of recommender systems
* Offline evaluation protocols and measures to assess the impact or recommenders
* User studies on the effects of recommenders on users, e.g., on decision making
* Simulation-based approaches to impact assessment
* Price- and profit-awareness of recommender systems
* Network effects of recommender systems
* Multi-stakeholder recommender systems
* Beyond accuracy optimization methods for recommender systems (e.g., business metrics, user satisfaction)
* Long-term implications of deployed recommender systems