Personalized Content Discovery using Content-Based Filtering
AI Applications — Module E Individual Project
A personalized content recommendation system designed to help users discover relevant articles, videos, and learning resources based on content similarity — demonstrating how modern platforms personalize user feeds to improve content discovery and engagement.
Recommind is a content-based recommendation system that analyses item features and user preferences to suggest relevant content. The project demonstrates how modern platforms like YouTube, Netflix, and Medium personalize user feeds using similarity-based filtering techniques.
| File | Description |
|---|---|
Recommind_Content_Recommendation_System.ipynb |
Main Jupyter Notebook — full project implementation |
- Problem definition and objective
- Data understanding and preparation
- Recommendation system design
- Core implementation
- Evaluation and analysis
- Ethical considerations
- Conclusion and future scope
User Input / Content Profile
↓
Feature Extraction
(TF-IDF / Embeddings)
↓
Similarity Computation
(Cosine Similarity)
↓
Ranked Recommendations
↓
Personalized Content Feed
- Content-based filtering using similarity metrics
- Personalized recommendations based on content features
- Supports articles, videos, and learning resources
- Clean evaluation and analysis of recommendation quality
- Ethical considerations for recommendation systems
pip install jupyter pandas numpy scikit-learnjupyter notebook Recommind_Content_Recommendation_System.ipynb| Tool | Purpose |
|---|---|
| Python | Core language |
| Jupyter Notebook | Development environment |
| Pandas | Data manipulation |
| Scikit-learn | ML & similarity computation |
| NLP techniques | Feature extraction |
This project is licensed under the MIT License.
🎯 Recommind — Built for educational purposes as part of AI Applications Module E