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 store knowledge of different task distributions in different mixed components and invoke this know-ledge when similar tasks occur,helping to alleviate the catastrophic forgetting problem in traditional algorithms.To estimate the posterior probabilities of model parameters,a more robust structured variational reasoning method is used to approximate the posterior values to avoid forgetting knowledge.Finally,VC-BML outperforms the baselines on all four datasets with non-stationary distributions.Compared with point-based estimation methods,VC-BML improves the robustness of the model and helps alle-viate the catastrophic forgetting problem.

Key words: Recommendation algorithm, Cold-start problem, Meta-learning, Dynamic Gaussian mixture model