Abstract: With the rapid development of Internet social media,using recommendation algorithms to effectively model and filter massive amounts of information has become the key to predict user behavior preferences,hot spot tendency,network security si-tuation and other issues.At the same time,with the development of deep learning,graph neural network model has achieved good results in solving the dense graph structure data in recommendation system.Collaborative filtering algorithm,as the most widely used recommendation algorithm,uses user-item group interaction data to predict users' future preferences and item ratings.However,existing recommendation algorithms still face the problems of data sparseness and cold start,and lack of a good quantification of uncertainty.This paper proposes an inductive matrix completion prediction fusion algorithm based on probabilistic meta-learning(MetaIMC),which re-characterizes meta-learning from the perspective of Bayesian inference,builds a robust GNN-meta-learning model,and makes full use of data priors to build solutions for learning new tasks from sparse data.Firstly,MetaIMC can effectively use variational Bayesian inference to obtain the prior distribution,alleviate the uncertainty and ambiguity in the meta-model task training,and further improve the generalization ability of the model.Secondly,MetaIMC can implement new user reco-mmendations and solve the cold start problem without any user side information.Finally,in the two scenarios of traditional matrix completion and user cold start,the performance of the model is evaluated by using three public datasets of Flixster,Douban and Yahoo_music,which verifies the effectiveness of MetaIMC on traditional matrix completion task,and achieves the best performance on the cold start problem.

Key words: Graph neural network, Matrix completion, Meta-learning, Recommendation system, Variational Bayesian inference