https://github.com/ankane/disco-go
Recommendations for Go using collaborative filtering
https://github.com/ankane/disco-go
recommendation-engine recommender-system
Last synced: 2 days ago
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Recommendations for Go using collaborative filtering
- Host: GitHub
- URL: https://github.com/ankane/disco-go
- Owner: ankane
- License: mit
- Created: 2023-09-29T20:45:26.000Z (about 2 years ago)
- Default Branch: master
- Last Pushed: 2025-05-20T21:51:13.000Z (5 months ago)
- Last Synced: 2025-07-01T04:03:50.717Z (3 months ago)
- Topics: recommendation-engine, recommender-system
- Language: Go
- Homepage:
- Size: 63.5 KB
- Stars: 24
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE.txt
Awesome Lists containing this project
README
# Disco Go
:fire: Recommendations for Go 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-go/actions)
## Installation
Run:
```sh
go get github.com/ankane/disco-go
```
## Getting Started
Import the package
```go
import "github.com/ankane/disco-go"
```
Prep your data in the format `userId, itemId, value`
```go
data := disco.NewDataset[string, string]()
data.Push("user_a", "item_a", 5.0)
data.Push("user_a", "item_b", 3.5)
data.Push("user_b", "item_a", 4.0)
```
IDs can be integers or strings
```go
data.Push(1, "item_a", 5.0)
```
If users rate items directly, this is known as explicit feedback. Fit the recommender with:
```go
recommender, err := disco.FitExplicit(data)
```
If users don’t rate items directly (for instance, they’re purchasing items or reading posts), this is known as implicit feedback. Use `1.0` or a value like number of purchases or page views for the dataset, and fit the recommender with:
```go
recommender, err := disco.FitImplicit(data)
```
Get user-based recommendations - “users like you also liked”
```go
recommender.UserRecs(userId, 5)
```
Get item-based recommendations - “users who liked this item also liked”
```go
recommender.ItemRecs(itemId, 5)
```
Get the predicted rating for a specific user and item
```go
recommender.Predict(userId, itemId)
```
Get similar users
```go
recommender.SimilarUsers(userId, 5)
```
## Examples
### MovieLens
Load the data
```go
data, err := disco.LoadMovieLens()
```
Create a recommender
```go
recommender, err := disco.FitExplicit(data, disco.Factors(20))
```
Get similar movies
```go
recommender.ItemRecs("Star Wars (1977)", 5)
```
## Storing Recommendations
Save recommendations to your database.
Alternatively, you can store only the factors and use a library like [pgvector-go](https://github.com/pgvector/pgvector-go). See an [example](https://github.com/pgvector/pgvector-go/blob/master/examples/disco/main.go).
## Algorithms
Disco uses high-performance matrix factorization.
- For explicit feedback, it uses the [stochastic gradient method with twin learners](https://www.csie.ntu.edu.tw/~cjlin/papers/libmf/mf_adaptive_pakdd.pdf)
- For implicit feedback, it uses the [conjugate gradient method](https://www.benfrederickson.com/fast-implicit-matrix-factorization/)
Specify the number of factors and iterations
```go
recommender, err := disco.FitExplicit(data, disco.Factors(8), disco.Iterations(20))
```
## Progress
Pass a callback to show progress
```go
callback := func(info disco.FitInfo) { fmt.Printf("%+v\n", info) }
recommender, err := disco.FitExplicit(data, disco.Callback(callback))
```
Note: `TrainLoss` and `ValidLoss` are not available for implicit feedback
## Validation
Pass a validation set with explicit feedback
```go
recommender, err := disco.FitEvalExplicit(trainSet, validSet)
```
The loss function is RMSE
## 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.
```go
recommender.UserRecs(newUserId, 5) // 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
```go
recommender.UserIds()
recommender.ItemIds()
```
Get the global mean
```go
recommender.GlobalMean()
```
Get factors
```go
recommender.UserFactors(userId)
recommender.ItemFactors(itemId)
```
## References
- [A Learning-rate Schedule for Stochastic Gradient Methods to Matrix Factorization](https://www.csie.ntu.edu.tw/~cjlin/papers/libmf/mf_adaptive_pakdd.pdf)
- [Faster Implicit Matrix Factorization](https://www.benfrederickson.com/fast-implicit-matrix-factorization/)
## History
View the [changelog](https://github.com/ankane/disco-go/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-go/issues)
- Fix bugs and [submit pull requests](https://github.com/ankane/disco-go/pulls)
- Write, clarify, or fix documentation
- Suggest or add new features
To get started with development:
```sh
git clone https://github.com/ankane/disco-go.git
cd disco-go
go mod tidy
go test -v
```