SSSxCCC / Recommender System
Licence: mit
A developing recommender system in tensorflow2. Algorithm: UserCF, ItemCF, LFM, SLIM, GMF, MLP, NeuMF, FM, DeepFM, MKR, RippleNet, KGCN and so on.
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python
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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 2.4.1
Run
Download data files and put 'ds' and 'kg' under 'Recommender_System/data' folder.
Open parent directory of current file as project in PyCharm, set up Python 3.8 interpreter and pip install tensorflow==2.4.1.
Go to Recommender_System/algorithm/xxx/main.py and run.
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 2.4.1
运行
下载数据文件并将文件夹'ds'和'kg'放到'Recommender_System/data'目录下。
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2.4.1版本。
到Recommender_System/algorithm/xxx/main.py源码文件下并点击运行。
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