Research on Intelligent Recommendation Algorithm Based on Deep Learning

With the wide application of intelligent recommendation system in e-commerce shopping, video websites and social media platforms, timeliness, accuracy, scalability and interpretability have gradually become important criteria to measure the excellence of a recommendation system.The most widely used recommendation system is collaborative filtering recommendation system. Its advantages include high accuracy, good real-time performance, and strong scalability, but there are still disadvantages, including cold start, data sparsity, and vulnerability. In the current flourishing of deep learning research, adding deep learning can fundamentally solve the problem, and better use the implicit and explicit information provided by past and current new users to bring more accurate and satisfying recommendations to users. This study analyzes and introduces the model building and recommendation algorithm logic of the representative recommendation system, and studies the feasibility of combining deep learning with artificial intelligence to optimize the new recommendation system.