Variational Continuous Bayesian Meta-learning Based Algorithm for Recommendation
Meta-learning methods have been introduced into recommendation algorithms in recent years to alleviate the problem of cold start.The existing meta-learning algorithms can only improve the ability of the algorithm to deal with a set of statically distributed data sets（tasks）.When faced with multiple...
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| Vydané v: | Ji suan ji ke xue Ročník 50; číslo 7; s. 66 - 71 |
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| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | Chinese |
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Chongqing
Guojia Kexue Jishu Bu
01.07.2023
Editorial office of Computer Science |
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| ISSN: | 1002-137X |
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| Abstract | Meta-learning methods have been introduced into recommendation algorithms in recent years to alleviate the problem of cold start.The existing meta-learning algorithms can only improve the ability of the algorithm to deal with a set of statically distributed data sets（tasks）.When faced with multiple data sets subject to non-stationary distribution, the existing models often have negative knowledge transfer and catastrophic forgetting problems, resulting in a significant decline in algorithm recommendation performance.This paper explores a recommendation algorithm based on variational continuous Bayesian Meta-learning（VC-BML）.Firstly, the algorithm assumes that the meta parameters follow the dynamic mixed Gaussian model, which makes it have a larger parameter space, improves the ability of the model to adapt to different tasks, and alleviates the problem of negative knowledge transfer.Then, the number of task clusters in VC-BML is flexibly determined by Chinese restaurant process（CRP）,which enables the model to |
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| AbstractList | Meta-learning methods have been introduced into recommendation algorithms in recent years to alleviate the problem of cold start.The existing meta-learning algorithms can only improve the ability of the algorithm to deal with a set of statically distributed data sets(tasks).When faced with multiple data sets subject to non-stationary distribution,the existing models often have negative knowledge transfer and catastrophic forgetting problems,resulting in a significant decline in algorithm recommendation performance.This paper explores a recommendation algorithm based on variational continuous Bayesian Meta-learning(VC-BML).Firstly,the algorithm assumes that the meta parameters follow the dynamic mixed Gaussian model,which makes it have a larger parameter space,improves the ability of the model to adapt to different tasks,and alleviates the problem of negative knowledge transfer.Then,the number of task clusters in VC-BML is flexibly determined by Chinese restaurant process(CRP),which enables the model to store Meta-learning methods have been introduced into recommendation algorithms in recent years to alleviate the problem of cold start.The existing meta-learning algorithms can only improve the ability of the algorithm to deal with a set of statically distributed data sets（tasks）.When faced with multiple data sets subject to non-stationary distribution, the existing models often have negative knowledge transfer and catastrophic forgetting problems, resulting in a significant decline in algorithm recommendation performance.This paper explores a recommendation algorithm based on variational continuous Bayesian Meta-learning（VC-BML）.Firstly, the algorithm assumes that the meta parameters follow the dynamic mixed Gaussian model, which makes it have a larger parameter space, improves the ability of the model to adapt to different tasks, and alleviates the problem of negative knowledge transfer.Then, the number of task clusters in VC-BML is flexibly determined by Chinese restaurant process（CRP）,which enables the model to |
| Author | Zhu, Huaijie Yin, Jian Zhu, Wentao Liu, Wei Liang, Shangsong |
| Author_xml | – sequence: 1 givenname: Wentao surname: Zhu fullname: Zhu, Wentao – sequence: 2 givenname: Wei surname: Liu fullname: Liu, Wei – sequence: 3 givenname: Shangsong surname: Liang fullname: Liang, Shangsong – sequence: 4 givenname: Huaijie surname: Zhu fullname: Zhu, Huaijie – sequence: 5 givenname: Jian surname: Yin fullname: Yin, Jian |
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| Copyright | Copyright Guojia Kexue Jishu Bu 2023 |
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| SubjectTerms | Algorithms Bayesian analysis Datasets Knowledge management Machine learning Mathematical models Parameter robustness recommendation algorithm|cold-start problem|meta-learning|dynamic gaussian mixture model Recommender systems |
| Title | Variational Continuous Bayesian Meta-learning Based Algorithm for Recommendation |
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