https://github.com/ankane/disco-rust
Recommendations for Rust using collaborative filtering
https://github.com/ankane/disco-rust
recommendation-engine recommender-system
Last synced: 6 months ago
JSON representation
Recommendations for Rust using collaborative filtering
- Host: GitHub
- URL: https://github.com/ankane/disco-rust
- Owner: ankane
- License: mit
- Created: 2021-12-01T08:56:14.000Z (almost 4 years ago)
- Default Branch: master
- Last Pushed: 2025-03-15T17:44:49.000Z (7 months ago)
- Last Synced: 2025-03-31T11:01:38.093Z (6 months ago)
- Topics: recommendation-engine, recommender-system
- Language: Rust
- Homepage:
- Size: 58.6 KB
- Stars: 41
- Watchers: 3
- Forks: 2
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE.txt
Awesome Lists containing this project
README
# Disco Rust
🔥 Recommendations for Rust using collaborative filtering
- Supports user-based and item-based recommendations
- Works with explicit and implicit feedback
- Uses high-performance matrix factorization
🎉 Zero dependencies
[](https://github.com/ankane/disco-rust/actions)
## Installation
Add this line to your application’s `Cargo.toml` under `[dependencies]`:
```toml
discorec = "0.2"
```
## Getting Started
Prep your data in the format `user_id, item_id, value`
```rust
use discorec::{Dataset, Recommender};
let mut data = Dataset::new();
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, strings, or any other hashable data type
```rust
data.push(1, "item_a".to_string(), 5.0);
```
If users rate items directly, this is known as explicit feedback. Fit the recommender with:
```rust
let recommender = Recommender::fit_explicit(&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:
```rust
let recommender = Recommender::fit_implicit(&data);
```
Get user-based recommendations - “users like you also liked”
```rust
recommender.user_recs(&user_id, 5);
```
Get item-based recommendations - “users who liked this item also liked”
```rust
recommender.item_recs(&item_id, 5);
```
Get the predicted rating for a specific user and item
```rust
recommender.predict(&user_id, &item_id);
```
Get similar users
```rust
recommender.similar_users(&user_id, 5);
```
## Examples
### MovieLens
Download the [MovieLens 100K dataset](https://grouplens.org/datasets/movielens/100k/).
Add these lines to your application’s `Cargo.toml` under `[dependencies]`:
```toml
csv = "1"
serde = { version = "1", features = ["derive"] }
```
And use:
```rust
use csv::ReaderBuilder;
use discorec::{Dataset, RecommenderBuilder};
use serde::Deserialize;
use std::fs::File;
#[derive(Debug, Deserialize)]
struct Row {
user_id: i32,
item_id: i32,
rating: f32,
}
fn main() {
let mut train_set = Dataset::new();
let mut valid_set = Dataset::new();
let file = File::open("u.data").unwrap();
let mut rdr = ReaderBuilder::new()
.has_headers(false)
.delimiter(b'\t')
.from_reader(file);
for (i, record) in rdr.records().enumerate() {
let row: Row = record.unwrap().deserialize(None).unwrap();
let dataset = if i < 80000 { &mut train_set } else { &mut valid_set };
dataset.push(row.user_id, row.item_id, row.rating);
}
let recommender = RecommenderBuilder::new()
.factors(20)
.fit_explicit(&train_set);
println!("RMSE: {:?}", recommender.rmse(&valid_set));
}
```
## Storing Recommendations
Save recommendations to your database.
Alternatively, you can store only the factors and use a library like [pgvector-rust](https://github.com/pgvector/pgvector-rust). See an [example](https://github.com/pgvector/pgvector-rust/blob/master/examples/disco/src/main.rs).
## 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
```rust
RecommenderBuilder::new()
.factors(8)
.iterations(20)
.fit_explicit(&train_set);
```
## Progress
Pass a callback to show progress
```rust
RecommenderBuilder::new()
.callback(|info| println!("{:?}", info))
.fit_explicit(&train_set);
```
Note: `train_loss` and `valid_loss` are not available for implicit feedback
## Validation
Pass a validation set with explicit feedback
```rust
RecommenderBuilder::new()
.callback(|info| println!("{:?}", info))
.fit_eval_explicit(&train_set, &valid_set);
```
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.
```rust
recommender.user_recs(&new_user_id, 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
```rust
recommender.user_ids();
recommender.item_ids();
```
Get the global mean
```rust
recommender.global_mean();
```
Get factors
```rust
recommender.user_factors(&user_id);
recommender.item_factors(&item_id);
```
## 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-rust/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-rust/issues)
- Fix bugs and [submit pull requests](https://github.com/ankane/disco-rust/pulls)
- Write, clarify, or fix documentation
- Suggest or add new features
To get started with development:
```sh
git clone https://github.com/ankane/disco-rust.git
cd disco-rust
cargo test
```