Research on Personalized Recommendation Method Based on Multi-source Information Learning

Personalized recommendation can effectively solve the negative impact of information overload on users and improve user experience in the big data environment. How to build an effective personalized recommendation system has become a common concern of industry and academia. Based on the basic idea of multi-layer perceptron, this paper constructs a personalized recommendation model of multi-source information. By introducing the relevant information of users and recommended items, iterative learning is carried out to improve the accuracy of user preference prediction. Combined with multi-layer perceptron method, the extended model is constructed. Based on TensorFlow framework, the batch data flow method is used to train the model. The implementation framework of the method is built, and the effectiveness of the method is verified by movielens data set. Experimental results show that the proposed method can effectively improve the accuracy of user preference prediction.