Compared with collaborative filtering, matrix factorization has better scalability and flexibility, but it is also troubled by data sparseness and cold start. Aiming at the above problems, a recommendation algorithm GNN_MF combining Graph Neural Network（GNN） and Probabilistic Matrix Factorization（PMF） is proposed. The algorithm uses GNN to model social network graphs and user item graphs, connects the two graphs internally, and learns the feature vector of the target user in the social space and item space. Then through Multi-Layer Perceptron（MLP）, the two feature vectors are connected in series to extract the user’s potential feature vector. Finally, it is integrated on the probability matrix factorization model to generate prediction scores. A large number of experiments on real data sets Epinions and Ciao show that the root mean square error and average absolute error of the GNN_MF algorithm are reduced by 2.91%, 3.10% and 4.83%, 3.84% respectively compared with traditional PMF. The effectiveness and feasibility of the GNN_MF algorithm in the recommendation system are verified.

%U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.2009-0013