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Have you ever wondered how Netflix suggests the perfect movie for your Friday night, how Amazon seems to know your shopping desires even before you do, or how your YouTube feed aligns with your interests? All of these are the magnificent effects of Recommendation Systems. So, in this blog post, let's know the trick of the magic.
How do these Work?
Well, the internet is of no power without data. Quite similarly, Recommendation Systems require tons of data. Not just a simple dataset, on which you can train and use forever! It must be updated with data every second. Let's assume you watched Tom & Jerry on YouTube and liked it. It makes a positive impact and tells the recommendation algorithm that you like Tom & Jerry. Then you watched another video, just for a few seconds, and closed it. This makes a negative impact and tells the recommender that you didn't like the video. But still, there are two types of recommendation systems:
The Two Types
This method makes recommendations based on user behavior. It identifies patterns by analyzing user-item interactions. If users A and B have similar preferences and have liked or purchased similar items, the system will recommend items liked by one to the other.
In this approach, recommendations are made based on the characteristics or content of the items and the user's profile. For instance, if you've shown interest in science fiction movies, the system will recommend more science fiction movies.
While recommendation systems offer incredible advantages, they're not without their challenges. One major hurdle is the "filter bubble" effect, where users are exposed only to content that aligns with their existing beliefs and preferences, potentially limiting diverse viewpoints. So, next time you're presented with that perfect movie or book, remember, it's not magic; it's the remarkable science of recommendation systems.
Till next time, Sree Teja Dusi.
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