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| The traditional recommendation system usually focuses on the coupling of feature information between users and items, but fails to effectively investigate the complex networks of users and items. At the same time, the graph algorithms are often used to analyze the point-edge relationships in networks, and we can combine as many network node features as possible through graph machine learning. To this end, in this paper, by combining the graph algorithm with the recommendation algorithm, prediction is conducted by embedding information. First, we employ the DeepFM and the GNNs to perform information mining of explicit and implicit features of the feature information and the heterogeneous structure network. Then, we combine the features of the two embedding layers to construct the final embedding vector. Finally, we use a multi-layer fully connected and activation function to predict the results. Two standard data sets are used in our experiment. The results show that the new model has the best performance in the recommended field. |
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| Keywords:Recommendation System; Graph Embedding; DeepFM |
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