Collaborative Filtering Recommendation Based on Generative Adversarial Network
A collaborative filtering recommendation algorithm based on generative adversarial network(GAN)and knowledge graph is proposed to solve the problem of data sparsity in personalized recommendation.The algorithm utilizes knowledge graph to extract semantic information from user's simple sequential behavior to construct user behavior paths.For sparse paths,a method of generating pseudo behavior routes based on sequence adversarial network is raised to in-crease the amount of user behavior routes to improve the recommendation performance.A series of experiments conducted on a real data set UserBehavior show that the proposed collaborative filtering recommendation algorithm is well behaved.The recommended number of users can be enhanced by about 104%,and the coverage can be improved by up to an order of magnitude.Additionally,the precision of the combined recommendation method incorporating the proposed algorithm into collaborative filtering is enhanced by 7.9%,2.6%,and 2.1%in three dimensions,respectively.
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