From the course: Building Recommender Systems with Machine Learning and AI
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Understanding you through implicit and explicit ratings - Python Tutorial
From the course: Building Recommender Systems with Machine Learning and AI
Understanding you through implicit and explicit ratings
- [Narrator] So how do recommender systems work? Well it comes down to understanding you. Not just you but every customer or visitor to a website or maybe even a network of websites that share data with each other. A recommender system starts with some sort of data about every user that it can use to figure out that user's individual tastes and interests then it can merge its data about you with the collective behavior of everyone else like you to recommend stuff you might like. But where does that data about your unique interests come from? One way to understand your users or customers is through explicit feedback. For example, asking users to rate an online course like this one on a scale of one to five stars or rating content they see with a like or a thumbs up or a thumbs down. In these cases you are explicitly asking your users do you like this thing you're looking at? And you use that data to build up a profile of that user's interests. The problem with explicit ratings or…
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