Research on Hybrid Collaborative Filtering Recommendation Algorithm Based on Deep Learning

In this paper, a hybrid collaborative filtering recommendation algorithm based on deep learning is proposed for the data sparsity and cold-start problems faced by traditional collaborative filtering recommendation algorithms. The algorithm combines the advantages of matrix decomposition and neural network, and extracts the deep features of users and items through deep learning techniques, which effectively improves the performance of the recommender system. The study uses MovieLens dataset for experimental validation, and the results show that the proposed algorithm outperforms traditional collaborative filtering algorithms in evaluation indexes such as accuracy, recall, and F1 value, which provides a new idea and method for solving the challenges faced by recommender systems.