Today, online platforms like Netflix offer thousands of movies and shows. These stores were a hit! That is, until the market was tired of limited selections and other physical constrictions. Let’s not date ourselves, but some may remember a time when we frequented video rental stores. How Netflix Slays the Recommendation Game Let’s take a deep dive into the Netflix recommendation system. In this article, we’ll learn how data science has enhanced our ability to choose and, frankly, our Saturday night binging options. We have a huge variety of choices because of how much is available through the Internet. The need for recommendation engines and personalization is a result of a phenomenon known as the “era of abundance”. This suggestion is the Netflix recommendation engine at work: it uses your past activity and returns movies and shows it thinks you will enjoy. When Netflix recommends The Office because I like Parks and Recreation, machine learning was behind that decision.
Machine learning gives the platform the ability to automate millions of decisions based off of user activities. Gone are the days of browsing the shelves in a Blockbuster on a Friday night (that is, if you even were alive when Blockbusters were around).īut how did the Netflix engineering team build a recommender engine? Netflix uses machine learning, a subset of artificial intelligence, to help their algorithms “learn” without human assistance. Companies like Netflix collect thousands of data points from several places to make suggestions to users with the help of a tool known as a recommender engine.
#MOVIE SUGGESTER MOVIE#
Consider how big data has changed our TV and movie experiences.
And since big data is so, well, big, we need optimized algorithms and high-powered computers to sift through it.Īdvances in data science have changed the way we communicate, share, and receive information. Any large quantity of information, from sports scores to social media posts, can be considered big data. But it would also be nice to see a rotten tomato score and brief synopsis, so you don’t gave to go searching for any additional information online.You may have heard the term “big data” thrown around, but do you understand what it is? Big data refers to massive datasets that can be used to reveal patterns and trends. Overall, the service is a huge leap forward from the traditional way of scrolling through a list of hundreds of movies at a time. That being said, there could still be some improvements that would enhance the experience even more.įor example, each response currently includes a link to a YouTube trailer so you can quickly check out the preview.
Since I usually tend to watch most movies that are similar to ones I like (i.e all Wes Anderson films) I suspect the algorithm specifically tries to recommend films you are likely to not have seen yet.Ĭurrently, requests take a few minutes to process (so don’t give up if you haven’t heard back yet), which may be because the startup noted that that some requests involve human input “to confirm what their data crunching gives them”. While I was at first skeptical of the startup’s lack of transparency around the recommendation process (mainly just because I like knowing how things work) most of that disappeared once I actually received some valuable suggestions.Īll three (decently complicated) questions I posed were answered with movies that I either had already seen and loved, or more frequently hadn’t heard of but looked great. The startup isn’t ready to give specific examples on how exactly its algorithm works, besides that it “uses a few different frameworks to detect patterns, attributes, and other factors” to pull movie options for viewers. After the superfluous pleasantries standard with all bots these days, And Chill asks you to tell it a movie you liked and why.įor example, I’d say “I liked Concussion because it was a true story about sports that also seemed like a medical mystery film”.Īnd Chill then analyzes these components and uses them to recommend similar films. The bot can be accessed via Facebook Messenger or SMS.
#MOVIE SUGGESTER HOW TO#
This is mainly due to shortfalls in natural language processing and the trial-and-error process of developers actually figuring out how users are going to use bots.īut one company, the slightly inappropriately named And Chill, has figured out how to build a bot that is actually well suited for Facebook’s chat-based interface.Īnd Chill is a movie recommendation engine that is eschewing the traditional “like/dislike” model of recommendation, instead pledging to dig deeper into the reasons a viewer would want to watch a certain movie. As we’ve discussed, Facebook’s bot platform is still in its early stages, and most apps aren’t ready yet to become part of your everyday life.
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