Visible to the public Recommender Systems As Multistakeholder Environments

TitleRecommender Systems As Multistakeholder Environments
Publication TypeConference Paper
Year of Publication2017
AuthorsAbdollahpouri, Himan, Burke, Robin, Mobasher, Bamshad
Conference NameProceedings of the 25th Conference on User Modeling, Adaptation and Personalization
Date PublishedJuly 2017
Conference LocationNew York, NY, USA
ISBN Number978-1-4503-4635-1
Keywordsadaptive filtering, collaborative filtering, Metrics, online advertising, pubcrawl, recommendation evaluation, recommender systems, resilience, Resiliency, Scalability

Recommender systems are typically evaluated on their ability to provide items that satisfy the needs and interests of the end user. However, in many real world applications, users are not the only stakeholders involved. There may be a variety of individuals or organizations that benefit in different ways from the delivery of recommendations. In this paper, we re-define the recommender system as a multistakeholder environment in which different stakeholders are served by delivering recommendations, and we suggest a utility-based approach to evaluating recommendations in such an environment that is capable of distinguishing among the distributions of utility delivered to different stakeholders.

Citation Keyabdollahpouri_recommender_2017