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Awesome Open Source

Recommender-System

A developing recommender system, implements in tensorflow 2.

Dataset: MovieLens-100k, MovieLens-1m, MovieLens-20m, lastfm, Book-Crossing, and some satori knowledge graph.

Algorithm: UserCF, ItemCF, LFM, SLIM, GMF, MLP, NeuMF, FM, DeepFM, MKR, RippleNet, KGCN and so on.

Evaluation: ctr's auc f1 and topk's precision recall.

Requirements

  • Python 3.8
  • Tensorflow 2.3.2

Run

Open parent directory of current file as project in PyCharm, set up Python 3.8 interpreter and pip install tensorflow==2.3.2.

Go to Recommender_System/algorithm/xxx/main.py and run.

MovieLens-20m is too large to upload. If you need it, download and put 'ml-20m' under 'Recommender_System/data/ds' folder.


Recommender-System推荐系统

这是一个正在开发的基于tensorflow2实现的推荐系统。

数据集:电影MovieLens-100k, MovieLens-1m, MovieLens-20m,音乐lastfm,书Book-Crossing,以及一些satori知识图谱。

算法:UserCF(基于用户的协同过滤), ItemCF(基于物品的协同过滤), LFM, SLIM, GMF, MLP, NeuMF, FM, DeepFM, MKR, RippleNet, KGCN等。

评估指标:点击率预测ctr的auc和f1,topk评估的准确率precision和召回率recall。

需求

  • Python 3.8
  • Tensorflow 2.3.2

运行

在PyCharm里面将此文件的父文件夹作为项目打开,设置好Python3.8的环境并使用pip安装tensorflow的2.3.2版本。

到Recommender_System/algorithm/xxx/main.py源码文件下并点击运行。

MovieLens-20m数据集太大了因此不被包含在此项目文件中,如果你需要这个数据集,下载MovieLens-20m并将'ml-20m'文件夹放到'Recommender_System/data/ds'目录下。


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