lyst/lightfm 源码下载_部署教程

项目概述

A Python implementation of LightFM, a hybrid recommendation algorithm.

项目地址

https://github.com/lyst/lightfm

项目页面预览

lyst/lightfm preview

关键指标

Stars：5058
主要语言：Python
License：Apache License 2.0
最近更新：2024-07-24T18:48:54Z
默认分支：master

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安装部署要点（README 精选）

Installation

Install from pip:

pip install lightfm

or Conda:

conda install -c conda-forge lightfm

Quickstart

Fitting an implicit feedback model on the MovieLens 100k dataset is very easy:

from lightfm import LightFM
from lightfm.datasets import fetch_movielens
from lightfm.evaluation import precision_at_k

# Load the MovieLens 100k dataset. Only five
# star ratings are treated as positive.
data = fetch_movielens(min_rating=5.0)

# Instantiate and train the model
model = LightFM(loss='warp')
model.fit(data['train'], epochs=30, num_threads=2)

# Evaluate the trained model
test_precision = precision_at_k(model, data['test'], k=5).mean()

常用命令（从 README 提取）

pip install lightfm

conda install -c conda-forge lightfm

@inproceedings{DBLP:conf/recsys/Kula15,
  author    = {Maciej Kula},
  editor    = {Toine Bogers and
               Marijn Koolen},
  title     = {Metadata Embeddings for User and Item Cold-start Recommendations},
  booktitle = {Proceedings of the 2nd Workshop on New Trends on Content-Based Recommender
               Systems co-located with 9th {ACM} Conference on Recommender Systems
               (RecSys 2015), Vienna, Austria, September 16-20, 2015.},
  series    = {{CEUR} Workshop Proceedings},
  volume    = {1448},
  pages     = {14--21},
  publisher = {CEUR-WS.org},
  year      = {2015},
  url       = {http://ceur-ws.org/Vol-1448/paper4.pdf},
}