📚 Advanced Recommender System

Welcome to the Advanced Recommender Systems!

---

🚀 Project Overview

Our goal is to create a comprehensive platform that excels in retrieving, classifying, ranking, and recommending documents tailored to user preferences. The pipeline of the project follows these key stages:

1. Data Collection & Preprocessing:

   • 📥 Data Collection: Gather academic paper data.
   • 🔧 Preprocessing: Prepare data for indexing.

2. Indexing & Retrieval Infrastructure:

   • 🗂️ Indexing: Develop an indexing system.
   • ✏️ Spell Correction: Integrate spell correction mechanisms.
   • 📊 Vector Space Models: Apply models for accurate search ranking.

3. Machine Learning & Clustering:

   • 🤖 Machine Learning: Implement classification algorithms.
   • 🧩 Clustering: Organize documents into clusters.

4. Web Crawling & Personalized Search:

   • 🌐 Web Crawling: Collect additional data from the web.
   • 🔍 Personalized Search: Develop advanced search and recommendation features.

5. Evaluation & Optimization:

   • 📈 Evaluation: Assess system performance using metrics.
   • 🔧 Optimization: Refine and improve system effectiveness.

---

🏗️ Phase 1: Data Acquisition and Indexing Infrastructure

Phase 1 focuses on laying the foundation for a robust information retrieval system by establishing an efficient data processing and indexing infrastructure.

Datasets

• Dataset: Scientific articles from Semantic Scholar.
• Dataset Category: Artificial Intelligence & Bioinformatics

Key Components

• 📂 Data Preprocessing & Preparation: Structure academic papers for efficient retrieval.
• 📚 Positional Index Construction: Create a positional index for precise document searches.
• 🔠 Spell Correction Integration: Integrate a bigram-based spell correction system.
• 🧮 Vector Space Modeling: Implement vector space models for effective document ranking:
  • ltn-lnn: Term frequency normalization model.
  • ltc-lnc: Term and document frequency adjustment model.
  • Okapi BM25: Probabilistic relevance ranking model.
• 📈 Evaluation Metrics: Assess system performance with metrics like MRR, Precision, Recall, F1 Score, MAP, and NDCG.

---

🧬 Phase 2: Machine Learning and Clustering for Document Retrieval

In Phase 2, we enhance retrieval capabilities through machine learning techniques, improving classification and clustering to refine the search system.

Key Components

• 📂 Dataset: Access the scientific articles dataset from Kaggle.
• 📊 Naive Bayes Classification: Implement a Naive Bayes classifier for document categorization.
• 🤖 Neural Network Classifier: Develop a neural network classifier for improved accuracy.
• 🧠 Large Language Models: Fine-tune a pre-trained model for advanced classification.
• 🧮 Hierarchical Clustering: Apply hierarchical clustering for document organization.

---

🛠️ Phase 3: Web Crawling, Link Analysis, and Personalized Search

Phase 3 centers on expanding the system’s capabilities with web crawling, link analysis, and advanced personalization features.

Key Components

• 🕷️ Web Crawling: Deploy a web crawler to gather academic articles and related data.
• 🔗 Link Analysis: Utilize PageRank and HITS algorithms to determine article importance.
• 📚 Content-Based Recommendation: Develop recommendations based on article content similarity.
• 🤝 Collaborative Filtering: Recommend articles based on the preferences of similar users.
• 🧪 Evaluation of Recommender Systems: Measure recommendation system performance using metrics like nDCG.

Final Product

Upon completion of Phase 3, the Advanced Recommender Systems will be a comprehensive tool that excels in retrieving, organizing, ranking, and recommending academic papers tailored to users' research needs.

---

📝 License

This project is licensed under the MIT License. For detailed information, please refer to the LICENSE file.

---

Thank you for your interest in the Advanced Recommender Systems! We hope this project serves as a valuable and engaging tool for your research and information retrieval needs. Happy exploring! 🚀