Package provides java implementation of content collaborative filtering for recommend-er system
npm install js-recommenderThe 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); 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:
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 });