Abstract: Negative sampling has a great impact on the accuracy of collaborative filtering algorithms,to solve the problem that the existing graph convolutional network lacks the exploration of negative sampling strategies,dynamic negative sampling-based graph convolution collaborative filtering recommendation model(DGCCF) is proposed.Firstly,in order to adapt more flexibly to the needs of different graph data,a normalization parameter is introduced in the graph convolutional network to adjust the influen-ce of the neighborhood.Secondly,a dynamic negative sampling strategy is proposed,which selects a set of negative samples from the item nodes that the user has not interacted with,and after graph convolution gets the negative sample score,selects the negative sample with the highest score as the hard negative sample,and finally uses the obtained hard negative sample and positive sample as samplesets to input the Bayesian personalized ranking function to optimize the model.Comparison experiments with the baseline model on the three public datasets Gowalla,Yelp2018 and Amazon-Book show that DGCCF is superior to existing baseline methods under multiple evaluation indicators.For example,compared to the optimal baseline,its recall rate increases by 0.3%,9.4%,and 10.6% respectively on three dataset.

Key words: Collaborative filtering, Graph convolutional neural network, Negative sampling, Recommendation system, Score prediction