ResearchRadar.ai is a web application submitted as an entry at PennApps XXIV, the prestigious collegiate hackathon hosted by the University of Pennsylvania in Fall 2023 which features 36 consecutive hours of in-person team coding. Our project was honored to receive the Best Design Hack award out of 114 total entries.
Every week, researchers invest significant time staying updated with the latest scientific publications. The painstaking process of searching for relevant papers diverts valuable time from their core research activities, potentially hindering scientific advancement. Over a year, this translates into considerable time and financial inefficiencies.
We believe that by leveraging artificial intelligence, our project can profoundly enhance the efficiency of this process. We have developed an AI-powered recommendation system that adeptly identifies and suggests high-quality, pertinent research papers.
Moreover, our system customizes its recommendations by analyzing each researcher's reading history, guaranteeing both relevance and significance. Notably, our platform delivers recommendations on a per-project basis. This means that even if a researcher is juggling multiple projects simultaneously, our recommendations remain distinct and organized, preventing any overlap or confusion.
- Using our AI-powered recommendation system, we help users find high-quality, relevant papers
- After a user finishes reading a paper, our system will recommend new research papers
- Search papers with AI reordering using Metaphor’s API
- Save research papers to a reading list
- See important keywords for each paper based on the YAKE Python library
- Annotate and view PDFs
- Create multiple projects to maintain an organized reading experience
AI-powered Recommendation system: We utilized the state-of-the-art “Neural Collaborative Filtering” algorithm through a Python library, which extends matrix factorization using a multi-layer perceptron. This approach enables us to recommend papers that align with the broader consensus of the research community.
Frontend: JavaScript, Chakra UI, Next.js, React.js
Backend: Python, Metaphor API, Microsoft Recommender library, PyTorch, Flask, YAKE (Yet Another Keyword Extractor Python library), Git
| Binh Nguyen | Derek Chen | Kingsley Zhong | Ziping Ye |
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| UX Designer, Frontend Engineer | Project Manager, Full Stack | Backend Engineer | ML Engineer |
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User Homepage with project selection to divide up your many research interests for the best personalization
New Project Onboarding
Project Page with project organization, where you can access:
(1) papers that our ML algorithm has recommended specifically for your project,
(2) past papers you have saved out of relevance and usefulness,
(3) papers you have bookmarked for future reading
Papers Page that list out results for your search query, or your saved and to-read papers
