Variational Continuous Bayesian Meta-learning Based Algorithm for Recommendation

• Published in: Ji suan ji ke xue Vol. 50; no. 7; pp. 66 - 71
• Main Authors: Zhu, Wentao, Liu, Wei, Liang, Shangsong, Zhu, Huaijie, Yin, Jian
• Format: Journal Article
• Language: Chinese
• Published: Chongqing Guojia Kexue Jishu Bu 01.07.2023; Editorial office of Computer Science
• Subjects: 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
• DOI: 10.11896/jsjkx.220900125

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