Recommender Systems As Multistakeholder Environments
| Title | Recommender Systems As Multistakeholder Environments |
| Publication Type | Conference Paper |
| Year of Publication | 2017 |
| Authors | Abdollahpouri, Himan, Burke, Robin, Mobasher, Bamshad |
| Conference Name | Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization |
| Date Published | July 2017 |
| Publisher | ACM |
| Conference Location | New York, NY, USA |
| ISBN Number | 978-1-4503-4635-1 |
| Keywords | adaptive filtering, collaborative filtering, Metrics, online advertising, pubcrawl, recommendation evaluation, recommender systems, resilience, Resiliency, Scalability |
| Abstract | 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. |
| URL | https://dl.acm.org/doi/10.1145/3079628.3079657 |
| DOI | 10.1145/3079628.3079657 |
| Citation Key | abdollahpouri_recommender_2017 |