NLP-Based Research Paper Recommendation System

Inspiration

The rapid growth of research publications makes it difficult for students and researchers to identify relevant papers efficiently. Traditional keyword-based search may overlook papers that discuss similar concepts using different terminology. We were inspired to build an intelligent system that understands the meaning of research queries and helps users discover relevant academic papers faster, reducing the time and effort required for literature review.

What it does

Our project recommends research papers based on the semantic meaning of user queries rather than relying only on exact keyword matches. It combines a local research paper repository with online academic sources to improve paper discovery. Key features include: Semantic search using NLP and Sentence Transformers. Similarity-based paper retrieval using FAISS. Integration with academic sources such as arXiv, Semantic Scholar, Crossref, CORE, and DBLP. Research paper recommendations with similarity scores. Paper summaries, comparison, and duplicate detection as planned system capabilities.

How we built it

We designed a hybrid architecture combining NLP-based semantic search, backend services, and a web interface. Sentence Transformers are used to generate semantic embeddings from research paper content, while FAISS supports efficient vector similarity search. FastAPI provides backend REST services for handling queries and research paper metadata. React.js is used to develop the interactive frontend. The system is designed to combine locally stored papers with academic API sources to support research discovery and repository updates.

Challenges we ran into

One of our main challenges was designing a recommendation approach that considers contextual meaning instead of exact keyword matches. Handling research paper metadata from different academic sources and maintaining a consistent data structure also required careful planning. Integrating the NLP pipeline with the FastAPI backend and React.js frontend presented additional coordination challenges. We also identified the need to evaluate recommendation relevance, retrieval efficiency, and API response performance to improve the overall system.

Accomplishments that we're proud of

We established a hybrid research paper recommendation system architecture that combines semantic search, a local repository, academic data sources, backend APIs, and a web interface. We divided the work among NLP and AI development, backend and database development, and frontend and system integration. We also developed a workflow for processing research queries and retrieving relevant papers based on semantic similarity, providing a foundation for more efficient literature review.

What we learned

Through this project, we gained experience in applying Natural Language Processing to a practical research problem. We learned about semantic embeddings, vector similarity search, FastAPI backend development, React.js frontend development, and academic API integration. We also improved our understanding of database design, GitHub collaboration, module integration, testing, and performance evaluation. Working as a team helped us understand how separate technical components can be combined into a complete application.

What's next for NLP-Based Research Paper Recommendation System

Our future plans include improving recommendation accuracy, expanding academic source integration, and adding more advanced research assistance features. We aim to develop AI-powered paper summaries and detailed paper comparisons, improve duplicate detection, and provide explanations for why each paper is recommended. We also plan to explore personalized recommendations, citation network visualization, multilingual search, and conversational research assistance to make the platform more useful for students, researchers, and academicians.

Built With

• ai
• arxiv
• crossref
• faiss
• fastapi
• javascript
• machine-learning
• natural-language-processing
• python
• react.js
• research-paper-recommendation
• text-processing