Juggler: Multi-Stakeholder Ranking with Meta-Learning

Online marketplaces must optimize recommendations with regards to multiple objectives, in order to fulfill expectations from a variety of stakeholders. This problem is typically addressed using Pareto Theory, which explores multiple objectives in a domain and identifies the objective vectors which yield the best performance. However, such approach is computationally expensive, and available commonly only through domain-specific solutions, which is not ideal for online marketplaces and their ever-changing business dynamics. We tackle these limitations by proposing a Meta-Learning framework to address the Multi-Stakeholder recommendation problem, which is able to dynamically predict the ideal settings on how business rules should be mingled into the final recommendations. The framework is designed to be generic enough to be leveraged in any item ranking domain and requires only the definition of a policy, i.e. a set of multi-objective metrics the meta-model should optimize for. The model finds the mapping between the search context and the corresponding best objective vectors. This way, the model is able to predict in real-time which is the best solution for any unforeseen search, and therefore adapt the recommendations on a search-level. We show that under this framework, the range of models one is able to build depends only on how many policies can be defined, thus offering a virtually unlimited way to address multi-objective problems. The experimental results showcase the generalization abilities of this framework and its highly predictive performance. Furthermore, the simulation results confirm the ability to approximate a policy’s expectation in most cases and hints to the potential to use this framework in many other item recommendation problems.