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Practical Data Science Cookbook by Abhijit Dasgupta, Benjamin Bengfort, Sean Patrick Murphy, Tony Ojeda

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Measuring the distance between users in the preference space

The two most recognizable types of collaborative filtering systems are user-based recommenders and item-based recommenders. If one were to imagine that the preference space is an N-dimensional feature space where either users or items are plotted, then we would say that similar users or items tend to cluster near each other in this preference space; hence, an alternative name for this type of collaborative filtering is nearest neighbor recommenders.

A crucial step in this process is to come up with a similarity or distance metric with which we can compare critics to each other or mutually preferred items. This metric is then used to perform pairwise comparisons of a particular user to ...

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