Multi-Objective Recommendations: A Tutorial
It serves as an educational resource for researchers and practitioners interested in multi-objective recommendations, but it is incremental as it summarizes existing knowledge rather than introducing novel findings.
This tutorial provides an overview of multi-objective optimization in recommender systems, addressing the emerging demand for handling multiple objectives like multi-stakeholder and multi-task scenarios, without presenting new experimental results or concrete numbers.
Recommender systems (RecSys) have been well developed to assist user decision making. Traditional RecSys usually optimize a single objective (e.g., rating prediction errors or ranking quality) in the model. There is an emerging demand in multi-objective optimization recently in RecSys, especially in the area of multi-stakeholder and multi-task recommender systems. This article provides an overview of multi-objective recommendations, followed by the discussions with case studies. The document is considered as a supplementary material for our tutorial on multi-objective recommendations at ACM SIGKDD 2021.