benfred/implicit 源码下载_部署教程

项目概述

Fast Python Collaborative Filtering for Implicit Feedback Datasets

项目地址

https://github.com/benfred/implicit

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关键指标

Stars：3761
主要语言：Python
License：MIT License
最近更新：2024-07-11T17:58:17Z
默认分支：main

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安装部署要点（README 精选）

Installation

Implicit can be installed from pypi with:

pip install implicit

Installing with pip will use prebuilt binary wheels on x86_64 Linux, Windows
and OSX. These wheels include GPU support on Linux.

Implicit can also be installed with conda:

# CPU only package
conda install -c conda-forge implicit

# CPU+GPU package
conda install -c conda-forge implicit implicit-proc=*=gpu

Basic Usage

import implicit

# initialize a model
model = implicit.als.AlternatingLeastSquares(factors=50)

# train the model on a sparse matrix of user/item/confidence weights
model.fit(user_item_data)

# recommend items for a user
recommendations = model.recommend(userid, user_item_data[userid])

# find related items
related = model.similar_items(itemid)

The examples folder has a program showing how to use this to compute similar artists on the
last.fm dataset.

For more information see the documentation.

Optimal Configuration

I’d recommend configuring SciPy to use Intel’s MKL matrix libraries. One easy way of doing this is by installing the Anaconda Python distribution.

For systems using OpenBLAS, I highly recommend setting ‘export OPENBLAS_NUM_THREADS=1’. This
disables its internal multithreading ability, which leads to substantial speedups for this
package. Likewise for Intel MKL, setting ‘export MKL_NUM_THREADS=1’ should also be set.

Released under the MIT License

常用命令（从 README 提取）

pip install implicit

# CPU only package
conda install -c conda-forge implicit

# CPU+GPU package
conda install -c conda-forge implicit implicit-proc=*=gpu