Research and Application of Personalized Recommendation Algorithm Based on Deep Learning Network

In today's digital age, information technology has made great progress, which has promoted the explosive growth of global data volume, and the problem of information overload has become increasingly serious. Faced with massive data information, users rely heavily on personalized recommendation algorithms to screen out the content that really meets their interests and needs. However, the traditional personalized recommendation algorithm needs to build user portraits with a large number of user behavior data, so that there are obvious shortcomings in dealing with data sparseness, cold start and other issues, which further affects the accuracy of the recommendation effect. In this regard, based on the actual needs, this paper will deeply explore the application feasibility of artificial intelligence and artificial neural network in this field, and propose a brand-new personalized recommendation algorithm model combined with deep learning network to solve the challenges faced by traditional recommendation algorithms. Practice has proved that the personalized recommendation model refers to CNN-LSTM to process diverse original information to obtain rich semantic features, and at the same time, it uses attention mechanism to obtain the attention vectors of users and projects. The user and project feature vectors generated by the combination of the two effectively improve the overall expressive ability of the model, which is beneficial to capture the interactive information between features and improve the accuracy of recommendation results.