From the course: Machine Learning and AI Foundations: Recommendations
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Use latent representations to find similar products
From the course: Machine Learning and AI Foundations: Recommendations
Use latent representations to find similar products
- [Instructor] Search engines are a common way that users discover new websites. When a first time user visits your website from a search engine, you don't know enough about the user yet to make personalized recommendations, until that user enters some product reviews our recommendation system can't recommend them anything yet. In this case, we can show the user similar products to the one they're already looking at. The goal is to keep them on the website and get them to look at more products. You've probably seen this feature on online shopping websites where it says, if you like this product you might also like these other products. With the product attributes calculated using matrix factorization, we can calculate product similarity. Let's look at find some more products dot PY. First, we'll load the movie ratings data set using panda's read CSV function. We'll also load the movie titles using read CSV into a data frame called movies DF. Then, we'll use the panda's pivot table…
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