A survey of recommender systems with multi-objective optimization

• Published in: Neurocomputing (Amsterdam) Vol. 474; pp. 141 - 153
• Main Authors: Zheng, Yong, Wang, David (Xuejun)
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 14.02.2022
• Subjects: Multi-objective; Multi-objective recommendation; Optimization; Pareto optimal; Recommender systems; Multi-objective recommendation; Multi-objective; Pareto optimal; Recommender systems; Optimization
• ISSN: 0925-2312; 1872-8286
• DOI: 10.1016/j.neucom.2021.11.041

Recommender systems have been widely applied to several domains and applications to assist decision making by recommending items tailored to user preferences. One of the popular recommendation algorithms is the model-based approach which optimizes a specific objective to improve the recommendation performance. These traditional recommendation models usually deal with a single objective, such as minimizing the prediction errors or maximizing the ranking quality of the recommendations. In recent years, there is an emerging demand for multi-objective recommender systems in which multiple objectives are considered and the recommendations can be optimized by the multi-objective optimization. For example, a recommendation model may be built by optimizing multiple metrics, such as accuracy, novelty and diversity of the recommendations. The multi-objective optimization methodologies have been well developed and applied to the area of recommender systems. In this article, we provide a comprehensive literature review of the multi-objective recommender systems. Particularly, 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 challenges or weaknesses, finally provide the guidelines and suggestions for the development of multi-objective recommender systems.