Build Your own Recommendation Engine-Netflix Demystified:Demo+Code

Build Your own Recommendation Engine-Netflix Demystified:Demo+Code

Build Your own Recommendation Engine-Netflix Demystified. An intro to recommender systems with live implementation.

What should I watch this evening?

How often you feel after a hectic day at work that what should I watch next? As for me — yes, and more than once. From Netflix to Prime Video, building robust movie recommendation systems is extremely important, given the huge demand for personalized content of modern consumers.

Once at home, sitting in front of the TV seems like a fruitless exercise with no control and no remembrance of the content we consumed. We tend to prefer an intelligent platform which understands our tastes and preferences and not just run on autopilot.

I have given a shot to building the recommendation engine based on my professional experience at Hotstar and binging experience on Netflix. I would consider this exercise fruitful if it can make you watch at least one movie based on its suggestions.

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Recommendation engine Interface working

Allow me to explain the basic logic of a recommendation engine before building one ourselves. There are broadly 3 algorithms which power a recco engine:

  1. Popular Movies: This algorithm handpicks trending content on the platform and recommends these movies to all the users. There is a major absence of personalization since every user would be shown the same content. It also implies the prominence of clickbait content with eye-catching thumbnail. This algorithm fails to showcase the vast repository of titles available on the platform.

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