Building the Recommendation Engine
Learn how to build a recommendation engine using the correlation between the datasets.
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Using correlation to rate movies
Say we have movie A and movie B. We’ve given a rating of 4.0 to movie A and the correlation between A and B is 1.0. Since 1.0 is the highest value, the movies are perfectly correlated, and the person who likes A should also like the B.
So our estimated score for B would be:
What if the correlation between movie A and movie B was -0.5? Our anticipated rating would be -2.0, which is very low. This means that those who enjoyed movie A probably wouldn’t like movie B.
To refresh, the steps are:
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Look at the scores of the movies we’ve rated.
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Check the rate movie’s correlation to the movies we haven’t rated.
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Find our anticipated score.
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Repeat steps 1–3 for all the three movies that we’ve rated.
Before we start on that, we’ve cleaned up corr_dict.py
for users.
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