https://github.com/chen0040/js-recommender
Package provides java implementation of content collaborative filtering for recommend-er system
https://github.com/chen0040/js-recommender
content-collaborative-filtering javascript recommendation-algorithms recommendation-engine recommender-system
Last synced: 2 months ago
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Package provides java implementation of content collaborative filtering for recommend-er system
- Host: GitHub
- URL: https://github.com/chen0040/js-recommender
- Owner: chen0040
- License: mit
- Created: 2017-05-23T06:28:27.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2017-06-05T00:38:34.000Z (over 8 years ago)
- Last Synced: 2025-08-09T09:06:17.935Z (2 months ago)
- Topics: content-collaborative-filtering, javascript, recommendation-algorithms, recommendation-engine, recommender-system
- Language: JavaScript
- Size: 56.6 KB
- Stars: 12
- Watchers: 1
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# js-recommender
Package provides java implementation of content collaborative filtering for recommend-er system
[](https://travis-ci.org/chen0040/js-recommender) [](https://coveralls.io/github/chen0040/js-recommender?branch=master)
# Install
```bash
npm install js-recommender
```
# Usage
The the direct use of the javascript in html can be found in [example.html](https://rawgit.com/chen0040/js-recommender/master/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
});
```