Collaborative Filtering

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A node module, that uses Collaborative filtering for the use of recommendations, it allows you out of the box, to use distance similarity / pearson/ others or custom one [depending on the need]

It is also provided with a full example of usage and dataset creation. I hope to provide a working stack [FE + BE] to use this with real-time and cache capabilities

using jSHint, matchdep , stream, grunt.js

Use this with my permission only

ToC

1. Main app

Main app

Install

npm install collaborative_filtering

place the distance.js where ever you want and include it, i've used an iOc style so you could adjust it and plug-it in the module

Initialization

we need to initialize the distance object, you can add any distance metric you wish to distance.js

var readers = require('./recommendations.js'), // creation of the dataset
	Collaborative = require('../lib/collaborative'),
	Distance = require('../lib/distance'),
	collab = new Collaborative(new Distance()),

after initialization, you need to create a multi-dimensional vector, an array of arrays: [[1,2],[1,4],[2,5],[5,9],...,[10,12]] just like the "creation of the data set line", you can find the model inside /models , it looks like:

in code we grab it via stream from a line-by-line [newline] structured flat file [so we won't have limit on memory space]

// people person1 = readers[0], person2 = readers[1], person7 = readers[6]

console.log('comparing ' + person1.getName() + ' and ' + person2.getName()) console.log('Distance correlation: ' + collab.simDistance(person1,person2)) console.log('Pearson correlation: ' + collab.simPearson(person1,person2))

console.log(collab.getSimiliarItems(readers, person1, 5)) console.log(collab.getRecommendations(readers,person7))

finally we run the collaborative filtering, for example "item-based":

[ { rating: 3.4682459444748344, id: 'And Then There Were None' },
  { rating: 3, id: 'A Tale of Two Cities' },
  { rating: 2.319573433326274, id: 'The Hobbit' } ]

## License

BSD -  ask for my permission