Package implements recommender system based on content collaborative filtering algorithm
npm install js-recommender 
``bash`
npm install js-recommender
The the direct use of the javascript in html can be found in example.html.
The sample code below tries to predict the missing rating of [user, movie] as shown in the table below:
`javascript
var jsrecommender = require("js-recommender");
var recommender = new jsrecommender.Recommender();
var table = new jsrecommender.Table();
// table.setCell('[movie-name]', '[user]', [score]);
table.setCell('Love at last', 'Alice', 5);
table.setCell('Remance forever', 'Alice', 5);
table.setCell('Nonstop car chases', 'Alice', 0);
table.setCell('Sword vs. karate', 'Alice', 0);
table.setCell('Love at last', 'Bob', 5);
table.setCell('Cute puppies of love', 'Bob', 4);
table.setCell('Nonstop car chases', 'Bob', 0);
table.setCell('Sword vs. karate', 'Bob', 0);
table.setCell('Love at last', 'Carol', 0);
table.setCell('Cute puppies of love', 'Carol', 0);
table.setCell('Nonstop car chases', 'Carol', 5);
table.setCell('Sword vs. karate', 'Carol', 5);
table.setCell('Love at last', 'Dave', 0);
table.setCell('Remance forever', 'Dave', 0);
table.setCell('Nonstop car chases', 'Dave', 4);
var model = recommender.fit(table);
console.log(model);
predicted_table = recommender.transform(table);
console.log(predicted_table);
for (var i = 0; i < predicted_table.columnNames.length; ++i) {
var user = predicted_table.columnNames[i];
console.log('For user: ' + user);
for (var j = 0; j < predicted_table.rowNames.length; ++j) {
var movie = predicted_table.rowNames[j];
console.log('Movie [' + movie + '] has actual rating of ' + Math.round(table.getCell(movie, user)));
console.log('Movie [' + movie + '] is predicted to have rating ' + Math.round(predicted_table.getCell(movie, user)));
}
}
`
To configure the recommender, can overwrite its parameters in its constructor:
`javascript``
var recommender = new jsrecommender.Recommender({
alpha: 0.01, // learning rate
lambda: 0.0, // regularization parameter
iterations: 500, // maximum number of iterations in the gradient descent algorithm
kDim: 2 // number of hidden features for each movie
});