Digital Cultural Resource Recommendation Method Integrating Knowledge Graph and Interest Preferences
In digital cultural resource recommendation,the precise matching between resources and user interests plays a key role.Although knowledge graphs effectively address the data sparsity and cold start problems in traditional recommendation algorithms,the static structure of knowledge graphs limits the understanding of the dynamic evolution of user interests.To address these issues,we propose a digital cultural resource recommendation method that integrates knowledge graphs and interest preferences(Knowledge Graph Interest Preferences,KGIP).Firstly,this method establishes the association between users and resources by constructing embedding repre-sentations of knowledge graphs.Secondly,it utilizes a long short-term memory network module to characterize user interests and explores complex features in users'long and short-term historical behaviors to more accurately capture user interest preferences.Finally,to fully utilize interest preferences and the association information between resources,the two feature representations are merged and fed into a multi-layer perceptron to learn the nonlinear structural features among different latent factors,introducing the Sigmoid activation function to obtain the final prediction results.Through multiple experiments on the Douban platform and the National Cultural Cloud platform dataset,the results show that KGIP performs well in digital cultural resource recommendation.
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