📚 Content-Based Recommender System

This project builds a recommendation system using content-based filtering. It uses Natural Language Processing (NLP), TF-IDF vectorization, and cosine similarity to recommend items based on a user's interaction history.

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🔧 Tools & Libraries

• Python
• Pandas & NumPy
• NLTK
• Scikit-learn
• SciPy

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💡 Key Features

• Text preprocessing and tokenization
• TF-IDF vectorizer to extract text features
• Cosine similarity to measure item similarity
• Custom recommendation class to return personalized suggestions
• Verbose mode to print ranked recommendations

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🧪 How It Works

1. Text data is vectorized using TfidfVectorizer
2. A user profile is generated from previously interacted items
3. Recommendations are generated using cosine similarity

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🚀 Topics

recommender-system, NLP, TF-IDF, cosine-similarity, machine-learning, content-based