Pricing
A Multi-Stakeholder Recommender System for Rewards Recommendations
This work describes a multi-stakeholder, multi-objective problem in the context of CommBank Rewards (CBR) and describes how a system that balances the objectives of the bank, its customers, and the many objectives from merchants into a single recommender system is developed.
Abstract
Australia’s largest bank, Commonwealth Bank (CBA) has a large data and analytics function that focuses on building a brighter future for all using data and decision science. In this work, we focus on creating better services for CBA customers by developing a next generation recommender system that brings the most relevant merchant reward offers that can help customers save money. Our recommender provides CBA cardholders with cashback offers from merchants, who have different objectives when they create offers. This work describes a multi-stakeholder, multi-objective problem in the context of CommBank Rewards (CBR) and describes how we developed a system that balances the objectives of the bank, its customers, and the many objectives from merchants into a single recommender system.