Building a New-York Times Article Recommendation Engine
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I built a scalable pipeline for a New York Times article recommendation engine using auto-summarisation and LDA modeling. I presented my results using a web interface designed with Flask and Material Design Lite
I used the New-York Times Article API to download metadata on articles
I also used BeautifulSoup to scrape the entire articles off of the website
|Recommendation||Latent Semantic Analysis (LSA)|
I only had some key words to create my word vectorizer, in order to solve that problem I also scraped the articles and summarized them. Then I used the summaries as well.
It was my first time working with Flask, and I found it to be a challenge
I was coding in Python 3 but it didn't accept the LSI tools I wanted to try.
To present my results I spent lots of time with Flask and didn't make a great presentation instead
I enjoyed learning how to build a recommendation engine and thinking about how other companies built their own.
I had conflicts with the Python version and using LSI tools.
What would I do differently
I would have spent more time on finding ways to improve the recommendation engine that the presentation.