Multi-Objective Recommendations

Abstract

The development of recommender systems usually deal with single-objective optimizations, such as minimizing prediction errors or maximizing the ranking quality. There is an emerging demand in multi-objective recommendations in which the recommendation list can be generated by optimizing multiple objectives. For example, researchers may balance different evaluation metrics (e.g., accuracy, novelty, diversity) in their models, or consider different objectives in a multi-task recommender. This tutorial provides an overview of the multi-objective optimization and its applications in the area of recommender systems. More specifically, we summarize the multi-objective optimization methods, identify the circumstances in which a multi-objective recommender system could be useful, and point out the challenges in multi-objective recommendations.