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

Installation

Add this line to your application’s Cargo.toml under [dependencies]:

discorec = "0.2"

Getting Started

Prep your data in the format user_id, item_id, value

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

data.push(1, "item_a".to_string(), 5.0);

If users rate items directly, this is known as explicit feedback. Fit the recommender with:

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:

let recommender = Recommender::fit_implicit(&data);

Get user-based recommendations - “users like you also liked”

recommender.user_recs(&user_id, 5);

Get item-based recommendations - “users who liked this item also liked”

recommender.item_recs(&item_id, 5);

Get predicted ratings for a specific user and item

recommender.predict(&user_id, &item_id);

Get similar users

recommender.similar_users(&user_id, 5);

Examples

MovieLens

Download the MovieLens 100K dataset.

Add these lines to your application’s Cargo.toml under [dependencies]:

csv = "1"
serde = { version = "1", features = ["derive"] }

And use:

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. See an example.

Algorithms

Disco uses high-performance matrix factorization.

• For explicit feedback, it uses the stochastic gradient method with twin learners
• For implicit feedback, it uses the conjugate gradient method

Specify the number of factors and iterations

RecommenderBuilder::new()
    .factors(8)
    .iterations(20)
    .fit_explicit(&train_set);

Progress

Pass a callback to show progress

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

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. It’s unable to make good recommendations without data on a user or item, which is problematic for new users and items.

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

recommender.user_ids();
recommender.item_ids();

Get the global mean

recommender.global_mean();

Get factors

recommender.user_factors(&user_id);
recommender.item_factors(&item_id);

References

• A Learning-rate Schedule for Stochastic Gradient Methods to Matrix Factorization
• Faster Implicit Matrix Factorization

History

View the changelog

Contributing

Everyone is encouraged to help improve this project. Here are a few ways you can help:

• Report bugs
• Fix bugs and submit pull requests
• Write, clarify, or fix documentation
• Suggest or add new features

To get started with development:

git clone https://github.com/ankane/disco-rust.git
cd disco-rust
cargo test