基于GNN的矩阵分解推荐算法

相较于协同过滤，矩阵分解有着更好的拓展性和灵活性，但同样受到数据稀疏和冷启动的困扰。针对上述问题，提出一种融合GNN和PMF的推荐算法GNN_MF。该算法通过神经网络对社交网络图以及用户项目图进行建模，将两个图内在的联系起来，学习目标用户在社会空间以及项目空间上的特征向量。通过MLP将两个特征向量串联提取用户的潜在特征向量，集成在概率矩阵分解模型上，产生预测评分。在真实数据集Epinions、Ciao上的大量实验表明，GNN_MF算法的均方根误差和平均绝对误差较传统PMF分别降低了2.91%、3.10%和4.83%、3.84%。验证了GNN_MF算法在推荐系统中的有效性以及可行性。

GNN-Based Matrix Factorization Recommendation Algorithm

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.