Investigating the Use of Long Short-Term Memory Networks for Dynamic Movie Recommendation Systems

The limits of conventional recommender systems, such collaborative filtering, have made it more difficult to find suitable content inside large digital movie archives given the explosive growth of online streaming services. A issue known as the cold start problem, these systems often suffer from data sparsity, insufficient scalability, and an incapacity to efficiently accommodate new clients or products. This work uses Long Short-Term Memory (LSTM) networks, a type of Recurrent Neural Network (RNN) adept in gathering long-term dependencies in sequential user activity, hence improving the accuracy and personalizing power of movie suggestions. Using the MovieLens data, we developed and trained an LSTM-based recommender model applying strict preprocessing, temporal modeling, and dynamic learning rate approaches. Experimental evaluations utilizing standard metrics like RMSE, MAE, and Precision@10 show that the LSTM-based system beats conventional approaches included as collaborative filtering, matrix factorization, and neural collaborative filtering. The results show that over time modeling user preferences greatly improves the quality of suggestions. The findings show that LSTM networks offer a feasible and efficient way to create scalable, context-aware recommender systems, therefore enhancing user pleasure and involvement in the digital entertainment industry.