Mood Based Movie Recommendation System

Mood Based Movie Recommendation System

Author Name : Purvi Rastogi, Rashi Gupta, Radhika Gupta, Riyanshi Goel, Md. Shahid

DOI: https://doi.org/10.56025/IJARESM.2025.130525134

The mood and ending based movie recommendation system is made to increase user experience by providing suggestions based on the mood of user. By taking the advantage of machine learning, natural language processing and collaboration filtering the model analyze key attributes such as genre, cast, mood, ending to create user-based recommendations. By using the dataset of 300 movies, the machine leaning model helps users to provide their mood like happy, sad, adventure and many more or the type of ending like tragic, open - ended etc, so that users can get handpicked movie suggestions. The machine learning model ensures that it provides a user-friendly interface which is spontaneous, making the content delivery flawless and smooth. Emphasizing personalization driven by AI, the mood-based movie recommendation system surpasses conventional rating-based suggestions or models, promoting a captivating, emotionally engaging and immersive viewing experience. Through the integration of data-informed insights, it provides a flexible strategy for movie suggestions, boosting customer satisfaction and increasing content interaction.In a similar vein, our movie recommendation system takes user preferences and observed behavior, interaction, contextual mood, and emotional state into account to generate better results. Every bit of user data collected feeds back into the system, which keeps on evolving and fine-tuning itself to preferences, suggesting personalized yet relatable movies that can trigger emotion.