SparkPapers

Inspiration

We wanted something like Netflix recommendations for research papers... because a nonzero number of us like reading those, and a certain ranty lecture from class stuck with us.

What it does

Presents the user with a paper title, author, conference, abstract, and URL to the paper. The user rates (on a scale of 1-3) how much they'd like to read the paper based on the abstract. A user's highly rated papers are noted and they can reference which papers (and respective URLs) they wanted to see.

How we built it

Front End:

• React
• Redux

Back End:

• MongoDB
• tears

Recommendations:

• SVD
• DBSCAN
• Data Structure Sins™

Challenges we ran into

• Losing four hours at the beginning on uncooperative (read: ignorance) web development and a bad idea. Solution: Ditch shenanigans (read: Node.js) and get a better idea.

Accomplishments that I'm proud of

• "Justin, what are you proud of?", "We're not using Node.js. I don't know. Make one up."
• "Will, what are you proud of?", "...", "I'll come back to you."
• "Eric, what are you proud of?", "You can make one up for me, too."
• "Audrey, what are you proud of?", "..."

What we learned

• Sometimes you need Data Structure Sins™ when time is not a thing on your side.
• A bad idea turned into a joke idea that turned into this idea... four hours into the hackathon.
• In Node, half your code will comprise of import statements.

What's next for SparkPapers

• LSI
• Transfer Learning: to generate synthetic data, which will serve as a better starting point.
• The use of ensemble methods; at the moment, we simply use DBSCAN.
• The full 36 GB of research papers and a V100.

Built With

• mongodb
• numpy
• pymongo
• react
• redux
• scipy
• sklearn