In the process of implementing recommendations, the browsing order of users is important information for the recommendation algorithm. The same user’s different preferences for items at different times also affect the recommendation results. Under the framework of the neural collaborative filtering model, this study proposes to integrate long short-term memory networks with generalized matrix factorization and capture both the user’s long-term and short-term preferences. The new model utilizes the strong fitting ability of long short-term memory networks to time series data to learn the user’s short-term preference and capture the long dependence relationship of the sequence. The user’s long-term preference is learned through generalized matrix factorization. The recommendation algorithm is thereby optimized, and the recommendation performance is improved. Experiments are carried out on the Movielens-1M dataset and the results show that the new model has a higher convergence rate and better recommendation performance.