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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Vydáno v:Ji suan ji ke xue Ročník 50; číslo 7; s. 66 - 71
Hlavní autoři: Zhu, Wentao, Liu, Wei, Liang, Shangsong, Zhu, Huaijie, Yin, Jian
Médium: Journal Article
Jazyk:čínština
Vydáno: Chongqing Guojia Kexue Jishu Bu 01.07.2023
Editorial office of Computer Science
Témata:
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
ISSN:1002-137X
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Shrnutí: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
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1002-137X
DOI:10.11896/jsjkx.220900125