Movie Recommendation System using Artificial Intelligence Techniques
Content-based movie recommendation with explainable scoring.
Case Study
Problem: Movie streaming platforms host vast libraries, making it difficult for users to discover relevant content.Traditional browsing methods often fail to capture user preferences and viewing habits.There was a need to practice implementing recommendation algorithms using artificial intelligence techniques.The project served as a way to learn data preprocessing, similarity metrics, and model evaluation in a real-world scenario.
Approach: Collected and cleaned movie metadata using pandas for preprocessing.Applied TF-IDF vectorization on features like genres, keywords, and descriptions to represent movies numerically.Calculated cosine similarity scores to measure closeness between movies.Generated top-N recommendations for each user query, along with simple explanations (e.g., similar genres or keywords).Experimented with hyperparameters and evaluation metrics to refine model quality.Packaged the system in a way that could be extended into a simple web demo for interactive use.
Impact: Strong interview talking point; easy to extend to web demo.