SimilariPy
High-performance KNN similarity functions in Python, optimized for sparse matrices.
SimilariPy is primarily designed for Recommender Systems and Information Retrieval (IR) tasks, but can be applied to other domains as well.
The package also includes a set of normalization functions useful for pre-processing data before the similarity computation.
The official documentations is available at 📘 SimilariPy Guide
🔍 Similarity Functions
SimilariPy provides a range of high-performance similarity functions for sparse matrices.
All functions are multi-threaded and implemented in Cython + OpenMP for fast parallel computation on CSR matrixes.
Core
- Dot Product – Simple raw inner product between vectors.
- Cosine – Normalized dot product based on L2 norm.
- Asymmetric Cosine – Skewed cosine similarity using an
alphaparameter. - Jaccard, Dice, Tversky – Set-based generalized similarities.
Graph-Based
- P3α – Graph-based similarity computed through random walk propagation with exponentiation.
- RP3β – Similar to P3α but includes popularity penalization using a
betaparameter.
Advanced
- S-Plus – A hybrid model combining Tversky and Cosine components, with full control over weights and smoothing.
For mathematical definitions and parameter details, see the 📘 SimilariPy Guide.
🧮 Normalization Functions
SimilariPy provides a suite of normalization functions for sparse matrix pre-processing.
All functions are implemented in Cython and can operate in-place on CSR matrixes for maximum performance and memory efficiency.
- L1, L2 – Applies row- or column-wise normalization.
- TF-IDF – Computes TF-IDF weighting with customizable term-frequency and IDF modes.
- BM25 – Applies classic BM25 weighting used in information retrieval.
- BM25+ – Variant of BM25 with additive smoothing for low-frequency terms.
For more details, check the 📘 SimilariPy Guide.
⚡ Performance
SimilariPy is built for speed on large sparse matrices:
- Cython + C++ + OpenMP — multi-threaded, GIL-free computation compiled to native code
- Cache-optimized — column-blocked accumulation with popularity reordering minimizes L2 cache misses
- Memory-efficient — float32 precision, pre-allocated buffers, top-K heap filtering
🚀 Getting Started
Here’s a minimal example to get you up and running with SimilariPy:
import similaripy as sim
import scipy.sparse as sps
# Create a random User-Rating Matrix (URM)
urm = sps.random_array((1000, 2000), density=0.025)
# Normalize the URM using BM25
urm = sim.normalization.bm25(urm)
# Train an item-item cosine similarity model
similarity_matrix = sim.cosine(urm.T, k=50)
# Compute recommendations for user 1, 14, 8
# filtering out already-seen items
recommendations = sim.dot_product(
urm,
similarity_matrix.T,
k=100,
target_rows=[1, 14, 8],
filter_cols=urm
)
For a more in-depth, end-to-end example with SimilariPy, see the notebook: MovieLens 32M Item-Item Recommender.
📦 Installation
SimilariPy can be installed from PyPI with:
pip install similaripy
Pre-built wheels are available for Python 3.11–3.15 on:
- Linux x86_64 and aarch64 (glibc 2.28+)
- macOS 15+ (Apple Silicon and Intel)
- Windows x64
No compiler is needed on these platforms. Elsewhere, pip builds the package from source (see below).
Free-threaded Python (3.14t, 3.15t) is supported: the GIL stays disabled. When calling SimilariPy from several Python threads at once, set num_threads on each call to avoid oversubscribing the CPU cores.
⚠️ On Windows, use the default format_output='coo' in all similarity functions, as 'csr' is currently not supported.
🔧 Build from source
Building from source is needed only when no wheel matches your platform, or to compile with CPU-specific optimizations. It requires a GCC-compatible compiler with OpenMP.
Ubuntu / Debian
Install the official dev-tools:
sudo apt update && sudo apt install build-essential
MacOS (Intel & Apple Silicon)
Install GCC with homebrew:
brew install gcc
Windows
Install the official Visual C++ Build Tools.
Optional: native CPU optimizations
Wheels target a generic CPU baseline. To build for your exact CPU (use -mcpu=native on ARM):
pip install similaripy --no-binary similaripy -C cmake.define.CMAKE_CXX_FLAGS="-march=native"
The speedup depends on the CPU: on Apple Silicon we measured none, so benchmark it on your workload.
Optional Optimization: Intel MKL for Intel CPUs
For Intel CPUs, using SciPy/Numpy with MKL (Math Kernel Library) is highly recommended for best performance. The easiest way to achieve this is to install them via Anaconda.
📦 Requirements
| Package | Version |
|---|---|
| python | >= 3.11 |
| numpy | >= 1.22.4 |
| scipy | >= 1.12.0 |
📜 History
This library originated during the Spotify Recsys Challenge 2018.
Our team, The Creamy Fireflies, faced major challenges computing large similarity models on a dataset with over 66 million interactions. Standard Python/Numpy solutions were too slow as a whole day was required to compute one single model.
To overcome this, I developed high-performance versions of the core similarity functions in Cython and OpenMP. Encouraged by my teammates, I open-sourced this work to help others solve similar challenges.
Thanks to my Creamy Fireflies friends for the support! 🙏
📄 License
This project is released under the MIT License.
🔖 Citation
If you use SimilariPy in your research, please cite:
@misc{boglio_simone_similaripy,
author = {Boglio Simone},
title = {bogliosimone/similaripy},
doi = {10.5281/zenodo.2583851},
url = {https://doi.org/10.5281/zenodo.2583851}
}
1 maintainer
Metadata
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Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Oct 11, 2026.
Transparency log
