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https://github.com/ankane/disco-node
Recommendations for Node.js using collaborative filtering
https://github.com/ankane/disco-node
recommendation-engine recommender-system
Last synced: about 10 hours ago
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Recommendations for Node.js using collaborative filtering
- Host: GitHub
- URL: https://github.com/ankane/disco-node
- Owner: ankane
- License: mit
- Created: 2023-09-10T21:18:24.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2024-07-13T23:26:26.000Z (6 months ago)
- Last Synced: 2024-12-26T13:08:05.299Z (7 days ago)
- Topics: recommendation-engine, recommender-system
- Language: JavaScript
- Homepage:
- Size: 17.6 KB
- Stars: 48
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE.txt
Awesome Lists containing this project
README
# Disco Node
:fire: Recommendations for Node.js using collaborative filtering
- Supports user-based and item-based recommendations
- Works with explicit and implicit feedback
- Uses high-performance matrix factorization
[](https://github.com/ankane/disco-node/actions)
## Installation
Run:
```sh
npm install disco-rec
```
## Getting Started
Create a recommender
```javascript
import { Recommender } from 'disco-rec';
const recommender = new Recommender();
```
If users rate items directly, this is known as explicit feedback. Fit the recommender with:
```javascript
recommender.fit([
{userId: 1, itemId: 1, rating: 5},
{userId: 2, itemId: 1, rating: 3}
]);
```
> IDs can be integers or strings
If users don’t rate items directly (for instance, they’re purchasing items or reading posts), this is known as implicit feedback. Leave out the rating.
```javascript
recommender.fit([
{userId: 1, itemId: 1},
{userId: 2, itemId: 1}
]);
```
> Each `userId`/`itemId` combination should only appear once
Get user-based recommendations - “users like you also liked”
```javascript
recommender.userRecs(userId);
```
Get item-based recommendations - “users who liked this item also liked”
```javascript
recommender.itemRecs(itemId);
```
Use the `count` option to specify the number of recommendations (default is 5)
```javascript
recommender.userRecs(userId, 3);
```
Get predicted ratings for specific users and items
```javascript
recommender.predict([{userId: 1, itemId: 2}, {userId: 2, itemId: 4}]);
```
Get similar users
```javascript
recommender.similarUsers(userId);
```
## Examples
### MovieLens
Load the data
```javascript
import { loadMovieLens } from 'disco-rec';
const data = await loadMovieLens();
```
Create a recommender and get similar movies
```javascript
const recommender = new Recommender({factors: 20});
recommender.fit(data);
recommender.itemRecs('Star Wars (1977)');
```
## Storing Recommendations
Save recommendations to your database.
Alternatively, you can store only the factors and use a library like [pgvector-node](https://github.com/ankane/pgvector-node). See an [example](https://github.com/pgvector/pgvector-node/blob/master/examples/disco/example.js).
## Algorithms
Disco uses high-performance matrix factorization.
- For explicit feedback, it uses [stochastic gradient descent](https://www.csie.ntu.edu.tw/~cjlin/papers/libmf/libmf_journal.pdf)
- For implicit feedback, it uses [coordinate descent](https://www.csie.ntu.edu.tw/~cjlin/papers/one-class-mf/biased-mf-sdm-with-supp.pdf)
Specify the number of factors and epochs
```javascript
new Recommender({factors: 8, epochs: 20});
```
If recommendations look off, trying changing `factors`. The default is 8, but 3 could be good for some applications and 300 good for others.
## Validation
Pass a validation set with:
```javascript
recommender.fit(data, validationSet);
```
## Cold Start
Collaborative filtering suffers from the [cold start problem](https://en.wikipedia.org/wiki/Cold_start_(recommender_systems)). It’s unable to make good recommendations without data on a user or item, which is problematic for new users and items.
```javascript
recommender.userRecs(newUserId); // returns empty array
```
There are a number of ways to deal with this, but here are some common ones:
- For user-based recommendations, show new users the most popular items
- For item-based recommendations, make content-based recommendations
## Reference
Get ids
```javascript
recommender.userIds();
recommender.itemIds();
```
Get the global mean
```javascript
recommender.globalMean();
```
Get factors
```javascript
recommender.userFactors(userId);
recommender.itemFactors(itemId);
```
## Credits
Thanks to [LIBMF](https://github.com/cjlin1/libmf) for providing high performance matrix factorization
## History
View the [changelog](https://github.com/ankane/disco-node/blob/master/CHANGELOG.md)
## Contributing
Everyone is encouraged to help improve this project. Here are a few ways you can help:
- [Report bugs](https://github.com/ankane/disco-node/issues)
- Fix bugs and [submit pull requests](https://github.com/ankane/disco-node/pulls)
- Write, clarify, or fix documentation
- Suggest or add new features
To get started with development:
```sh
git clone https://github.com/ankane/disco-node.git
cd disco-node
npm install
npm test
```