From the course: Building a Recommendation System with Python Machine Learning and AI
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Evaluating similarity based on correlation
From the course: Building a Recommendation System with Python Machine Learning and AI
Evaluating similarity based on correlation
- [Instructor] The next type of recommendation system to look at is correlation-based recommendation systems. These recommenders offer basic form of collaborative filtering. That's because with correlation-based recommendation systems, items are recommended based on similarities in their user reviews. In this sense, they do take user preferences into account. In these systems, you use Pearson r correlation to recommend an item that is most similar to the item a user has already chosen. In other words, to recommend an item that has a review score that correlates with another item that a user has already chosen, based on similarity between user ratings. Just to refresh on Pearson r, the Pearson r correlation coefficient is a measure of linear correlation between two variables, or in this case two items ratings. The Pearson correlation coefficient is represented by the symbol r, and with an r value that's close to one or negative one, then you know you have a strong linear relationship…
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