AI Research Assistant

Utilizes Semantic Scholar and arXiv APIs to provide real-time access to millions of academic papers. The system employs a hybrid search algorithm that combines keyword matching with semantic understanding to deliver relevant results, making it distinct in its ability to interpret user queries contextually. This allows users to find papers that are not only keyword-relevant but also conceptually aligned with their research interests.

Employs algorithms to analyze citation networks of academic papers, allowing users to track how often a paper has been cited and by whom. This capability leverages graph-based data structures to visualize citation relationships, providing insights into the impact and relevance of research over time. This is particularly useful for understanding trends and influential works in a specific field.

Facilitates the extraction of full-text PDFs from open-access sources like arXiv and Wiley. This capability employs a combination of web scraping and API calls to retrieve documents, ensuring that users can access the complete content of papers without navigating away from the platform. This is particularly beneficial for users needing direct access to research documents for in-depth reading.

Generates recommendations for academic papers based on user queries and previously viewed papers using machine learning algorithms. This capability analyzes user behavior and content similarity to suggest relevant papers, enhancing the research experience by providing tailored content. The underlying model continuously learns from user interactions to improve recommendation accuracy over time.