Comprehensive Review of Meta-Learning Methods for Cold-Start Issue in Recommendation Systems
Document Type
Article
Publication Date
1-1-2025
Abstract
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.
Keywords
Metalearning, Recommender systems, Systematic literature review, Systematics, Focusing, Adaptation models, Transfer learning, Standards, Planning, History, Recommendation systems, cold-start, meta-learning, systematic review, strengths and limitations
Divisions
Information
Funders
inistry of Education, Malaysia (FRGS/1/2024/ICT03/UM/02/2) ; (FP059-2024),University of Malaya (UM) International Collaboration Grant (ST005-2023),Faculty of Computer Science,Universitas Brawijaya, Indonesia (01050/UN10.A0101/B/TU.01.00.1/2024)
Publication Title
IEEE Access
Volume
13
Publisher
Institute of Electrical and Electronics Engineers
Publisher Location
445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA