Variational Continuous Bayesian Meta-learning Based Algorithm for Recommendation - Zhu, Wentao; Liu, Wei; Liang, Shangsong; Zhu, Huaijie; Yin, Jian (Ji suan ji ke xue, 2023)
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