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Movie Recommendations with k-Nearest Neighbors and Cosine Similarity

Introduction

The k-nearest neighbors (k-NN) algorithm is among the simplest algorithms in the data mining field. Distances / similarities are calculated between each element in the data set using some distance / similarity metric ^^[1]^ that the researcher chooses (there are many distance / similarity metrics), where the distance / similarity between any two elements is calculated based on the two elements' attributes. A data element’s k-NN are the k closest data elements according to this distance / similarity.

1. A distance metric measures distance; the higher the distance the further apart the neighbors. A similarity metric measures similarity; the higher the similarity the closer the neighbors.

First of all you need to be able to run MAMP in port 80. This is a "heat check" if you don't have any process jamming http ports. You can check it like this:

sudo lsof | grep LISTEN

If you do happen to have any process with something like this *:http (LISTEN), you are in trouble. Before with adventure check if it isn't MAMP itself (yeah, you should close that beforehand)

ps <pid of that process>

If you don't see MAMP, you are in good hands, I have just the thing for you:

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####IREUS Recommendation Engine for stores as SaaS

http://www.ireus.net/

####Plista

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	<title>AWS Cognito + Facebook Login JavaScript Example</title>
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	# Backup
	docker exec CONTAINER /usr/bin/mysqldump -u root --password=root DATABASE > backup.sql

	# Restore
	cat backup.sql \| docker exec -i CONTAINER /usr/bin/mysql -u root --password=root DATABASE

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	hide_icons() {
	defaults write com.apple.finder CreateDesktop false
	killall Finder
	}

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