A sophisticated Information Retrieval System designed to filter and return only genuinely relevant information, addressing the challenge of 'Fair Information Retrieval.' It achieves this through a powerful combination of TF-IDF, BM-25, and Cosine Similarity for refined ranking. Implemented with a Python backend and a Streamlit frontend.
Showcases expertise in IR algorithms and practical full-stack system development.
Key Technical Achievements & Demonstrated Skills:
- Comprehensive IR Modeling: Designed and implemented a robust information retrieval model capable of efficiently processing and indexing diverse datasets.
- Advanced Ranking Algorithm Integration: Leveraged a powerful combination of industry-standard ranking algorithms:
- TF-IDF (Term Frequency-Inverse Document Frequency): For vector space model transformation and initial relevance scoring.
- BM-25 (Okapi BM25): A probabilistic ranking function used for fine-tuning relevance based on query terms.
- Cosine Similarity: Applied for computing similarity scores between queries and documents in the vector space.
- Full-Stack Development: Engineered the system with a robust Python backend for all retrieval logic and data processing, complemented by an interactive and user-friendly frontend built with Streamlit.
- Data Preprocessing & Query Optimization: Implemented techniques for efficient data preprocessing and query analysis to enhance retrieval accuracy.
This system demonstrates a strong foundation in Information Retrieval theory, search algorithm implementation, and practical full-stack development.