This video was very interesting, and neat to see some of the complicated mathematics behind such a simple, at our fingertips app. From the presentation, I had a few key takeaways:

  1. Hadoop is highly revered. Well, it’s at least most frequently discussed and reveled over. But?????, it was interesting to hear that Spark was able to handle the data and requirements for recommendation system much faster than Hadoop!

  2. The fact that really huge datasets undermine the executing functions was interesting.

  3. Manually tagging attributes was mind-blowing! So, Spotify has a group of experts that assign music specific attributes. I figured this was all done with Machine Learning.

  4. Collaborative Filtering (user-based) was cool to hear. After learning about collaborative filtering systems in class, this seems intuitive for best music recommendations.

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