Enhancing user experience with content-based filtering and NLP-driven insights.

Skillset Used : Content-Based Filtering, Natural Language Processing (NLP), Tokenization, Stemming (Porter Stemmer), Bag of Words (BoW), CountVectorizer, Cosine Similarity, Data Cleaning & Preprocessing

🔍 What I did

• Built a movie recommendation system using content-based filtering on data from the TMDB website.
• Processed and cleaned a dataset of 4,500+ movies, creating tags based on overview, keywords, cast, crew, and genre.
• Tokenized text-based features, applied Porter Stemmer to unify word variations, and implemented a Bag of Words model to extract key movie descriptors.
• Used CountVectorizer to create numerical feature vectors and computed cosine similarity to identify and recommend similar movies.

📈 Impact & Insights

• Enhanced personalization, delivering recommendations based on movie content rather than user behavior.
• Refined data preprocessing techniques, improving the quality of feature extraction for better model performance.
• Optimized similarity detection, ensuring accurate recommendations tailored to user interests.
• Scalable approach, allowing easy expansion with additional movie metadata for richer recommendations.

🚀 Learning Outcomes

• Strengthened expertise in NLP techniques for text-based recommendations.
• Gained hands-on experience in vectorization, similarity measures, and feature engineering.
• Improved understanding of how movie recommendation systems work behind the scenes.
• Explored the power of content-based filtering in contrast to collaborative filtering approaches.