Recommender systems have been widely applied to several domains and applications. Traditional recommender systems usually deal with a single objective, such as minimizing the prediction errors or maximizing the ranking of the recommendation list. There is an emerging demand for multi-objective optimization so that the development of recommendation models can take multiple objectives into consideration.
Currently, the multi-objective optimization methodologies have been well developed, and they have been reused in the area of recommender systems. This tutorial aims to provide a comprehensive introduction of the multi-objective optimization and the multi-objective recommender systems. More specifically, we identify the circumstances in which a multi-objective recommender system could be useful, summarize the methodologies and evaluation approaches in these systems, point out existing problems by a critical analysis, and provide guidelines for the usage of multi-objective optimization in recommender systems.
Schedule: Aug. 14, 2021 9:00 AM - 12:00 PM (Singapore Time)
Part 1: Multi-Objective Optimization (MOO) by Dr. David (Xuejun) Wang
Schedule: 09:00 - 10:10 (Singapore Time)
QA: 10:10 - 10:20 (Singapore Time)
Break: 10:20 - 10:30 (Singapore Time)
Part 2: Recommender Systems with MOO by Dr. Yong Zheng
Schedule: 10:30 - 11:50 (Singapore Time)
QA: 11:50 - 12:00 (Singapore Time)
Illinois Institute of Technology, USA
Principal Data Scientist
Morningstar, Inc., USA