Comprehensive Review of Meta-Learning Methods for Cold-Start Issue in Recommendation Systems
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...
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| Format: | Article |
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2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10857336/ |
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| author | Jamallah M. Zawia Maizatul Akmar Binti Ismail Mohammad Imran Buce Trias Hanggara Diva Kurnianingtyas Silvi Asna Quang Tran Minh |
| author_facet | Jamallah M. Zawia Maizatul Akmar Binti Ismail Mohammad Imran Buce Trias Hanggara Diva Kurnianingtyas Silvi Asna Quang Tran Minh |
| author_sort | Jamallah M. Zawia |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d9a802ae8542458d8ed2c8f82d264e3d |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-d9a802ae8542458d8ed2c8f82d264e3d2025-02-11T00:00:53ZengIEEEIEEE Access2169-35362025-01-0113246222464110.1109/ACCESS.2025.353602510857336Comprehensive Review of Meta-Learning Methods for Cold-Start Issue in Recommendation SystemsJamallah M. Zawia0https://orcid.org/0009-0003-7767-5794Maizatul Akmar Binti Ismail1https://orcid.org/0000-0003-1877-7128Mohammad Imran2https://orcid.org/0000-0001-9485-1524Buce Trias Hanggara3Diva Kurnianingtyas4https://orcid.org/0000-0002-0865-7790Silvi Asna5https://orcid.org/0000-0002-4811-0909Quang Tran Minh6https://orcid.org/0000-0003-1408-2919Department of Information Systems, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, MalaysiaDepartment of Information Systems, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, MalaysiaDepartment of Information Technology, Faculty of Information and Communication Technology, Balochistan University of Information Technology, Engineering and Management Sciences, Quetta, PakistanDepartment of Information System, Faculty of Computer Science, Universitas Brawijaya, Malang, IndonesiaDepartment of Informatics Engineering, Faculty of Computer Science, Universitas Brawijaya, Malang, IndonesiaDepartment of Economics, Faculty of Economic and Business, Universitas Brawijaya, Malang, IndonesiaDepartment of Information Systems, Faculty of Computer Science and Engineering, Ho Chi Minh City University of Technology (HCMUT), Ho Chi Minh City, VietnamThe 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.https://ieeexplore.ieee.org/document/10857336/Recommendation systemscold-startmeta-learningsystematic reviewstrengths and limitations |
| spellingShingle | Jamallah M. Zawia Maizatul Akmar Binti Ismail Mohammad Imran Buce Trias Hanggara Diva Kurnianingtyas Silvi Asna Quang Tran Minh Comprehensive Review of Meta-Learning Methods for Cold-Start Issue in Recommendation Systems IEEE Access Recommendation systems cold-start meta-learning systematic review strengths and limitations |
| title | Comprehensive Review of Meta-Learning Methods for Cold-Start Issue in Recommendation Systems |
| title_full | Comprehensive Review of Meta-Learning Methods for Cold-Start Issue in Recommendation Systems |
| title_fullStr | Comprehensive Review of Meta-Learning Methods for Cold-Start Issue in Recommendation Systems |
| title_full_unstemmed | Comprehensive Review of Meta-Learning Methods for Cold-Start Issue in Recommendation Systems |
| title_short | Comprehensive Review of Meta-Learning Methods for Cold-Start Issue in Recommendation Systems |
| title_sort | comprehensive review of meta learning methods for cold start issue in recommendation systems |
| topic | Recommendation systems cold-start meta-learning systematic review strengths and limitations |
| url | https://ieeexplore.ieee.org/document/10857336/ |
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