Personalization is an essential characteristic of recommender systems; they are designed to find items that meet user needs and tastes. However, the receiver of the recommendation may not always be the only party whose goals are relevant in recommendation computation. Also, in many contexts, such as digital advertising, the value associated with recommendation delivery may need to be included in the recommendation calculation. The purpose of this workshop is to bring together researchers to formulate a common vision for research progress in this new area.
Topics of interest include:
Reciprocal recommendation: the context in which a recommendation must satisfy both the target user and recommended party — person-to-person recommendation as a prime example (e.g., for employment, dating, or connecting on a social site).
Strategic recommendation: the context in which the system has its own recommendation objectives, not necessarily shared by the user, for example, pedagogical goals in educational recommendation.
Multi-objective recommendation, especially where the objectives represent the interests of different parties, and where the objectives may vary by context.
Recommendation in multi-sided platforms.
Mechanisms to represent and resolve conflicting interests among recommendation stakeholders.
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