Google Scholar Research Collaboration Platform
Overview
A Django web application that helps researchers discover relevant scholarly work by indexing the Google Scholar feed and surfacing personalized results. NLP techniques extract key information from abstracts and keywords so that searches return contextual, not just lexical, matches. Outcome: Gave researchers a faster way to find relevant papers and collaborators, with personalized ranking based on their interests.
Architecture & Pipeline
flowchart LR
n0["User QueryResearcher search"]
n1["Google Scholar IntegrationExternal feed"]
n2["NLTK ExtractionAbstracts · keywords"]
n3["Personalized RankerPer-user interests"]
n4["Search Results UIDjango"]
n0 --> n1
n1 --> n2
n2 --> n3
n3 --> n4
classDef step0 fill:#f1f5f9,stroke:#64748b,color:#1e293b,stroke-width:2px,rx:10,ry:10;
classDef step1 fill:#ecfeff,stroke:#06b6d4,color:#1e293b,stroke-width:2px,rx:10,ry:10;
classDef step2 fill:#f0fdfa,stroke:#0d9488,color:#1e293b,stroke-width:2px,rx:10,ry:10;
classDef step3 fill:#ecfdf5,stroke:#10b981,color:#1e293b,stroke-width:2px,rx:10,ry:10;
classDef step4 fill:#fffbeb,stroke:#f59e0b,color:#1e293b,stroke-width:2px,rx:10,ry:10;
class n0 step0;
class n1 step1;
class n2 step2;
class n3 step3;
class n4 step4;
End-to-end flow derived from this project's scope and tech stack. Tap View Fullscreen for a larger view, or scroll horizontally on small screens.
Key Features
- Web app for indexing and searching scholarly articles
- Google Scholar data integration
- NLP-based keyword and abstract extraction
- Personalized result ranking per user
- Tech Stack:** Python, Django, NLTK