What it does

Our deepReccomender leverages cutting edge natural language processing techniques to recommend real-estate domain relevant news articles given an initial text. The crux of our deepReccomender is a machine learning model trained during the hackathon to assign multiple real-estate specific document tags to an input text. The tags, predicted from contextualized text representations, are compared in Jaccard similarity to known tags of documents in a fixed collection to produce reccomendations.

How I built it

Our solution leverages a three-tiered architecture powered by orchestrated containerization (docker). Raw data is shared with a Flask python API which assigns worker threads to requests received from a front end React powered application.

Challenges I ran into

  • Working on a large project with multiple contributers and maintaining clean code. ## Accomplishments that I'm proud of
  • We defined the problem we wanted to solve from the start, narrowed down the scope of solution and delivered.

What's next for deepRecommender

  • Increased predictive performance
  • Expansion to a larger database of indexed articles to recommend.

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