Paper Info
Title
Multi-stakeholder recommendations: case studies, methods and challenges
Abstract
Recommender systems are able to produce a list of recommended items tailored to user preferences, while the end user is the only stakeholder in the system. However, there could be multiple stakeholders in several applications or domains, e.g., e-commerce, advertising, educations, dating, job seeking, and so forth. Recommendations are necessary to be produced by balancing the needs of different stakeholders. This tutorial covers the introductions to multi-stakeholder recommender systems (MSRS), introduces multiple case studies, discusses the corresponding methods and challenges to develop MSRS. Particularly, a demo based on the MOEA framework will be given in the talk by using a speed-dating dataset.
Year
DOI
Venue
2019
10.1145/3298689.3346951
Proceedings of the 13th ACM Conference on Recommender Systems
Keywords
Field
DocType
multi-criteria, multi-objective learning, multi-stakeholder, recommender system, utility
Stakeholder,Computer science,Knowledge management,Artificial intelligence,Machine learning
Conference
ISBN
Citations 
PageRank 
978-1-4503-6243-6
0
0.34
References 
Authors
0
1
Name
Order
Citations
PageRank
Yong Zheng120118.20