Package implements recommender system based on content collaborative filtering algorithm

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Package provides java implementation of content collaborative filtering for recommend-er system
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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:
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);

predicted_table = recommender.transform(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:
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