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.

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