irspack — implicit recommenders for practitioners
irspack helps practitioners build, evaluate, and tune recommenders from implicit feedback such as clicks, views, saves, and purchases.
It is designed for the work that happens before a recommender reaches production: establishing strong baselines, comparing algorithms under the same evaluation protocol, and tuning promising candidates without rewriting the pipeline for every model.
- Practical recommenders including iALS, SLIM, item/user KNN, P3alpha, RP3beta, TopPop, and experimental feature-aware iALS
- Fast C++/Eigen implementations for core training and evaluation operations
- Consistent evaluation and Optuna-backed hyperparameter tuning
- Utilities for converting business IDs to sparse matrices and back
- Support for cold-start experiments with user and item side features
Read the documentation, see which recommender to try first, or start with your own interaction data.
Installation
irspack requires Python 3.9 or later. Install the published package with:
pip install irspack
Pre-built wheels are published for supported Linux, macOS, and Windows platforms. See Installing from source if a wheel is not available for your environment or if you want CPU-specific compiler optimizations.
Quickstart
irspack consumes a SciPy sparse matrix whose rows are users and whose columns are items. The values represent interaction strength; binary values are a good default for events such as clicks or purchases.
import numpy as np
import pandas as pd
from irspack import IALSRecommender, df_to_sparse
events = pd.DataFrame(
{
"user_id": ["alice", "alice", "bob", "bob", "carol", "carol"],
"item_id": ["A", "B", "B", "C", "A", "D"],
}
)
# Rows and columns in X correspond to user_ids and item_ids, respectively.
X, user_ids, item_ids = df_to_sparse(events, "user_id", "item_id")
model = IALSRecommender(X, n_components=8).learn()
# Recommend unseen items for Alice. Scores for already-seen items are -inf.
alice_index = np.flatnonzero(user_ids == "alice")[0]
scores = model.get_score_remove_seen(np.array([alice_index]))[0]
top_items = item_ids[np.argsort(scores)[::-1][:2]]
print(top_items)
For an offline comparison, split interactions into training and validation matrices and evaluate every candidate with the same evaluator:
from irspack import Evaluator, IALSRecommender, TopPopRecommender
from irspack.split import rowwise_train_test_split
X_train, X_validation = rowwise_train_test_split(
X, test_ratio=0.2, ceil_n_heldout=True, random_state=0
)
evaluator = Evaluator(X_validation, cutoff=3)
for recommender_class in (TopPopRecommender, IALSRecommender):
recommender = recommender_class(X_train).learn()
print(recommender_class.__name__, evaluator.get_score(recommender)["ndcg"])
For timestamped events, prefer a temporal holdout over a random split. The using your own data guide shows the full workflow, including stable ID mappings and leakage-aware evaluation.
Which model should I try first?
| Situation | Good starting point |
|---|---|
| Sanity check and popularity baseline | TopPopRecommender |
| Strong general-purpose collaborative filtering | IALSRecommender |
| Explainable item-to-item recommendations | CosineKNNRecommender |
| Sparse implicit-feedback data | RP3betaRecommender or SLIMRecommender |
| Cold-start users/items with side information | Feature-aware iALS |
There is no universally best recommender. Start with a cheap baseline, compare a small set of candidates using a split that reflects the product scenario, then tune the winner. See the model selection guide for trade-offs and optional dependencies.
Hyperparameter tuning
Every tunable recommender exposes the same Optuna-backed interface:
best_params, trials = IALSRecommender.tune(
X_train,
evaluator,
n_trials=20,
random_seed=0,
)
best_model = IALSRecommender(X_train, **best_params).learn()
Iterative recommenders can use intermediate validation scores to stop unpromising trials early.
Optional recommenders
BPRFMRecommender wraps LightFM and requires a separate LightFM installation:
pip install lightfm
MultVAERecommender requires jax, jaxlib, dm-haiku, and optax. Follow
the JAX installation guide
if you need GPU support.
Installing from source
A source build requires a C++17 compiler. To compile using the instruction set available on the current machine:
CFLAGS="-march=native" pip install git+https://github.com/tohtsky/irspack.git
If installation fails, please open an issue and include your OS, Python version, CPU architecture, and the complete build error.
Development
This repository uses uv for reproducible local development:
uv sync
uv run pytest
Install documentation dependencies and build the site with:
uv sync --group docs
uv run sphinx-build -b html docs/source docs/_build/html
Use uv lock --upgrade only when intentionally updating dependencies. The lock
file is committed so local development and CI use the same versions.
1 maintainer
Metadata
1 maintainer
Release files for irspack 0.5.2
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Transparency logRelease files / irspack-0.5.2-cp310-cp310-musllinux_1_2_x86_64.whl
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Transparency logRelease files / irspack-0.5.2-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
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