Certain Investigation in Optimizing User Experience through LSTM-Enhanced Movie Recommendations

Entertainment plays a vital role in daily life, offering enjoyment, education, and enrichment. Movies, in particular, are globally popular, serving as both entertainment and educational tools. They offer insights into history and moral lessons for children. Categorized into various genres—comedy, thriller, drama, romance, horror, and more—movies provide diverse experiences. With unlimited choices available online, selecting a movie has become a challenge. Streaming platforms like Netflix, YouTube, and Disney+ recommend content using collaborative filtering, content-based filtering, or hybrid systems. However, these face issues like scalability, sparsity, and cold start problems. This paper explores how Long Short-Term Memory (LSTM), a type of Recurrent Neural Network, enhances recommendations by analyzing user history, improving personalized suggestions, and addressing data scarcity challenges.