Jamallah M. Zawia, Maizatul Akmar Binti Ismail, Mohammad Imran, Buce Trias Hanggara, Diva Kurnianingtyas, Silvi Asna, Quang Tran Minh
The cold-start issue in recommendation systems refers to the challenge of recommending items or users when minimal or no prior data is available. Meta-learning methods have emerged as a response to this challenge due to their ability to transfer prior knowledge to recommendation tasks. However, meta-learning techniques are still new, and a general literature review is missing. This paper reviews the existing literature on meta-learning techniques specifically designed to solve the cold-start issue in recommender systems. A systematic review of the literature published between 2018 and June 2024 was conducted, identifying only experimental papers that use meta-learning methods to solve the cold-start issue. Advances, strengths, and weaknesses of such methods were analyzed, and possible research directions for the future were identified. The results demonstrate the application of model-independent meta-learning (MAML) and other techniques such as optimization-based methods, few-shot learning frameworks, and gradient-based meta-learning methods to solve the cold start problem. It also shows how meta-learning improvement can be achieved by combining different strategies, using transfer learning, and effectively implementing the strategy. Some of the areas for further research are also listed. In summary, this work contributes to verifying the central mechanisms of the recently proposed meta-learning models for further research in dealing with the cold-start issue. Insights into user-item interactions, critical applications, and evaluation standards are provided. © 2025 IEEE.
Universiti Malaya, Faculty of Computer Science and Information Technology, Department of Information Systems, Kuala Lumpur, 50603, Malaysia; Universitas Brawijaya, Faculty of Computer Science, Department of Information System, Malang, 65154, Indonesia; Balochistan University of Information Technology, Engineering and Management Sciences, Faculty of Information and Communication Technology, Department of Information Technology, Quetta, 87300, Pakistan; Universitas Brawijaya, Faculty of Computer Science, Department of Informatics Engineering, Malang, 65154, Indonesia; Universitas Brawijaya, Faculty of Economic and Business, Department of Economics, Malang, 65154, Indonesia; Ho Chi Minh City University of Technology (HCMUT), Faculty of Computer Science and Engineering, Department of Information Systems, Ho Chi Minh City, 700000, Viet Nam; Vietnam National University Ho Chi Minh City (VNU-HCM), Faculty of Information Technology (VNU-HCM), Thu Duc District, Ho Chi Minh City, 700000, Viet Nam
