fastslim

SLIM (Sparse Linear Methods, Ning & Karypis 2011) learns a sparse, non-negative item-item weight matrix W from a user-item matrix X and scores users with one sparse product, X @ W. Every recommendation is explainable: "because you interacted with these items".

fastslim is a Rust implementation behind a small Python API. The solver is exact (the returned weights satisfy the optimality conditions of the objective below), deterministic (bit-identical output for any thread count) and fast: MovieLens 1M fits in about a second.

Install

pip install fastslim  # or: uv add fastslim

Wheels for Linux (x86_64, aarch64), macOS and Windows, Python 3.10 and newer. Runtime dependencies are NumPy and SciPy.

Usage

import numpy as np
from scipy import sparse

import fastslim

rng = np.random.default_rng(0)
X = sparse.csr_matrix((rng.random((2000, 300)) < 0.03).astype(np.float64))  # users x items

W = fastslim.fit(X, lambd=2.0, beta=2.0)      # items x items, sparse, zero diagonal
top = fastslim.recommend(W, X[:5], k=10)       # top-10 unseen items for five users
scores = fastslim.predict(W, X[0])             # raw scores for one user

The same solver as an estimator:

from fastslim import SLIM

model = SLIM(lambd=2.0, beta=2.0).fit(X)
model.recommend(X[0], k=5)
model.weights, model.n_passes, model.converged  # fitted state

fit takes any SciPy sparse matrix or array, or a dense array; values must be finite and non-negative. fastslim.metrics provides precision_at_k, recall_at_k and ndcg_at_k against a held-out sparse matrix. The full reference is in docs/api.md.

Hyperparameters

• lambd, the L1 penalty, is compared against co-occurrence counts: item k can enter item i's model only if (X.T @ X)[i, k] >= lambd. On MovieLens-sized data useful values are single digits. Raise it for a sparser W.
• beta, the L2 penalty, shrinks weights and conditions the problem; any beta > 0 makes the solution unique.
• max_iter and tol stop each item once a full pass moves no weight by tol. The defaults (1000, 1e-4) converge all of MovieLens 1M. Items that run out of budget raise fastslim.ConvergenceWarning; SLIM.converged tells which ones.
• n_threads changes speed only. The result does not depend on it.

Performance

MovieLens, 80/20 split, k=10, against implicit's ALS with 64 factors. SLIM uses lambd=2, beta=2, max_iter=50.

Dataset	Model	Fit time	precision@10	recall@10	ndcg@10
ML-100k	fastslim	0.17 s	0.3391	0.2236	0.4069
ML-100k	implicit ALS	0.58 s	0.2878	0.2017	0.3433
ML-1M	fastslim	2.55 s	0.3620	0.1652	0.4100
ML-1M	implicit ALS	4.45 s	0.3465	0.1621	0.3915

Reproduce with uv sync --group bench and uv run python benchmarks/benchmark.py --dataset 1m --baseline als --markdown.

How it works

For every item i independently, the solver minimises

0.5 * ||x_i - X w||^2 + lambd * sum_k w_k + (beta / 2) * sum_k w_k^2
subject to  w >= 0,  w_i = 0

by non-negative coordinate descent on the Gram matrix P = X.T @ X: only neighbours with P_ik >= lambd can be non-zero, residuals are updated incrementally, and an active set is iterated until a full verification pass confirms optimality. P is accumulated in a fixed order and each item is solved by a single thread, which is what makes the output deterministic. The derivation is in docs/algorithm.md.

Development

uv sync                          # builds the extension, installs dev tools
uv run pytest -q -m "not slow"   # Python tests
cargo test                       # Rust tests

CONTRIBUTING.md covers layout, linting, benchmarks and releases; CHANGELOG.md lists what changed between versions.

License

Apache-2.0. See LICENSE.