Experimental Design of Personalized Recommendation System Based on Deep Learning

This article is targeted at practical teaching for undergraduate students majoring in artificial intelligence. Utilizing industry-level datasets, it constructs a recommendation system experiment through deep learning, aiming to enhance students' engineering practical skills. The experiment compares the application of two algorithms, collaborative filtering and deep neural networks, in personalized recommendation. Simulation experiments validate the effectiveness of the algorithms, especially the advantages of deep learning methods in terms of comprehensive recommendation performance, laying a solid foundation for students in the application of deep learning and recommendation system engineering.