# 召回模型库
## 简介
我们提供了常见的召回任务中使用的模型算法的PaddleRec实现, 单机训练&预测效果指标以及分布式训练&预测性能指标等。实现的召回模型包括 [SR-GNN](gnn)、[GRU4REC](gru4rec)、[Sequence Semantic Retrieval Model](ssr)、[Word2Vector](word2vec)、[Youtube_DNN](youtube_dnn)、[ncf](ncf)。
模型算法库在持续添加中，欢迎关注。
## 目录
* [整体介绍](#整体介绍)
* [召回模型列表](#召回模型列表)
* [使用教程](#使用教程)
* [训练 预测](#训练 预测)
* [效果对比](#效果对比)
* [模型效果列表](#模型效果列表)
## 整体介绍
### 召回模型列表
| 模型 | 简介 | 论文 |
| :------------------: | :--------------------: | :---------: |
| Word2Vec | word2vector | [Distributed Representations of Words and Phrases and their Compositionality](https://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf)(2013) |
| GRU4REC | SR-GRU | [Session-based Recommendations with Recurrent Neural Networks](https://arxiv.org/abs/1511.06939)(2015) |
| Youtube_DNN | Youtube_DNN | [Deep Neural Networks for YouTube Recommendations](https://static.googleusercontent.com/media/research.google.com/zh-CN//pubs/archive/45530.pdf)(2016) |
| SSR | Sequence Semantic Retrieval Model | [Multi-Rate Deep Learning for Temporal Recommendation](http://sonyis.me/paperpdf/spr209-song_sigir16.pdf)(2016) |
| NCF | Neural Collaborative Filtering | [Neural Collaborative Filtering](https://arxiv.org/pdf/1708.05031.pdf)(2017) |
| GNN | SR-GNN | [Session-based Recommendation with Graph Neural Networks](https://arxiv.org/abs/1811.00855)(2018) |
下面是每个模型的简介（注：图片引用自链接中的论文）
[Word2Vec](https://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf):
[GRU4REC](https://arxiv.org/abs/1511.06939):
[Youtube_DNN](https://static.googleusercontent.com/media/research.google.com/zh-CN//pubs/archive/45530.pdf):
[SSR](http://sonyis.me/paperpdf/spr209-song_sigir16.pdf):
[NCF](https://arxiv.org/pdf/1708.05031.pdf):
[GNN](https://arxiv.org/abs/1811.00855):
## 使用教程
### 训练 预测
```shell
python -m paddlerec.run -m paddlerec.models.recall.word2vec # word2vec
python -m paddlerec.run -m paddlerec.models.recall.ssr # ssr
python -m paddlerec.run -m paddlerec.models.recall.gru4rec # gru4rec
python -m paddlerec.run -m paddlerec.models.recall.gnn # gnn
python -m paddlerec.run -m paddlerec.models.recall.ncf # ncf
python -m paddlerec.run -m paddlerec.models.recall.youtube_dnn # youtube_dnn
```
## 效果对比
### 模型效果列表
| 数据集 | 模型 | HR@10 | Recall@20 |
| :------------------: | :--------------------: | :---------: |:---------: |
| DIGINETICA | GNN | -- | 0.507 |
| RSC15 | GRU4REC | -- | 0.670 |
| RSC15 | SSR | -- | 0.590 |
| MOVIELENS | NCF | 0.688 | -- |
| -- | Youtube | -- | -- |
| 1 Billion Word Language Model Benchmark | Word2Vec | -- | 0.54 |