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KNN Similarity Algorithms for Collaborative Filtering Models

Project description


Fast Python KNN-Similarity algorithms for Collaborative Filtering models in Recommender System and others.

This project provides fast Python implementations of several different popular KNN (K-Nearest Neighbors) similarity algorithms for Recommender System models.

Base similarity models:

  • Dot Product
  • Cosine
  • Asymmetric Cosine
  • Jaccard
  • Dice
  • Tversky

Graph-based similarity models:

  • P3Alpha
  • RP3Beta

Advanced similarity model:

  • S-Plus

[ Complete documentation coming soon... ] [ TODO ]

All models have multi-threaded routines, using Cython and OpenMP to fit the models in parallel among all available CPU cores.

To install:

pip install similaripy

Basic usage:

import similaripy as sim
import scipy.sparse as sps

# create a random user-rating matrix (URM)
urm = sps.random(1000, 2000, density=0.025)

# train the model with 50 knn per item 
model = sim.cosine(urm.T, k=50)

# recommend items for users 1, 14 and 8
user_recommendations = dot_product(urm, model, target_rows=[1,14,8], k=100)

For more information see the documentation. [ TODO ]


Package Version
numpy >= 1.14
scipy >= 1.0.0
tqdm >= 4.19.6
scikit-learn >= 0.19.1
cython >= 0.28.1

NOTE: In order to compile the Cython code it is required a GCC compiler with OpenMP (on OSX it can be installed with homebrew: brew install gcc).

This library has been tested with Python 3.6 on Ubuntu, OSX and Windows.

Optimal Configuration

I recommend configuring SciPy/Numpy to use Intel's MKL matrix libraries. The easiest way of doing this is by installing the Anaconda Python distribution.

Future work

I plan to release in the next future some utilities:

  • Utilities for sparse matrices
  • Pre-processing / post-processing functions (TF-IDF, BM25 and more)
  • New similarity functions ( good ideas are welcome :) )


The idea of build this library comes from the RecSys Challenge 2018 organized by Spotify.

My team, the Creamy Fireflies, had problem in compute very huge similarity models in a reasonable time (66 million of interactions in the user-rating matrix) and using python and numpy were not suitable since a full day was required to compute one single model.

As a member of the the team I spent a lot of hours to develop these high-performance similarities in Cython to overcome the problem. At the end of the competition, pushed by my team friends, I decide to release my work to help people that one day will encounter our same problem.

Thanks to my Creamy Fireflies friends for support me.


Released under the MIT License

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