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A Recommendation System is a tool designed to suggest items, such as movies, products, or services, based on user preferences and behavior. This system analyzes data like ratings, genres, and viewing trends to provide personalized recommendations. It helps users discover new items they might enjoy while also predicting the profitability of movies or similar content.
• Personalized Recommendations: Tailored suggestions based on user preferences and viewing history.
• Movie Profitability Prediction: Predicts the commercial success of movies using historical data.
• Similar Film Suggestions: Identifies movies with similar themes, genres, or styles to the ones users already like.
• Data-Driven Insights: Utilizes databases like Movie_Infor to ensure accurate and relevant recommendations.
What data does the Recommendation System use?
The system uses databases like Movie_Infor, which includes details such as genres, ratings, and popularity trends.
How accurate are the recommendations?
Accuracy depends on the quality of the input data and the user's viewing history. Consistently rating movies helps improve recommendation accuracy.
Can the system handle new or lesser-known movies?
Yes, the system continuously updates its database and can include new releases. However, recommendations for lesser-known movies may rely more on genre or thematic similarities.