Graph Convolution Network Recommender Rystem with Feature Embedding
DOI:
https://doi.org/10.54097/fcis.v1i1.1497Keywords:
Graph Neural Network, Recommender System, Collaborative FilteringAbstract
Recommender system has become one of the most important parts of information services on the Internet today the model based on graph neural network has been proved to be a very effective method in collaborative filtering recommender system [1,15]. However, the recommender system based on graph network usually only uses the interactive relationship between users and items, and their feature information is often not used effectively. In this work, we combine the use of feature information with graph convolutional network to design a new recommendation algorithm, Graph Convolution Network recommender system with Feature Embedding, GCNFE. Tests on the Movie lens and Last-FM datasets show that the new model performs better than previous models in most cases.
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References
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