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未验证 提交 37dfcd3b 编写于 作者: C Chengmo 提交者： GitHub
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Co-authored-by: Nwuzhihua02 <wuzhihua02@baidu.com>
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README.md
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- 包含内容理解、匹配、召回、排序、 多任务、重排序等多个任务的完整推荐搜索算法库 - 包含内容理解、匹配、召回、排序、 多任务、重排序等多个任务的完整推荐搜索算法库
| 方向 | 模型 | 单机CPU | 单机GPU | 分布式CPU | 分布式GPU | 模型来源 | | 方向 | 模型 | 单机CPU | 单机GPU | 分布式CPU | 分布式GPU | 论文 |
| :------: | :-----------------------------------------------------------------------: | :-----: | :-----: | :-------: | :-------: | :--------------------------------------------------------------------------------------------------------------------------------------: | | :------: | :-----------------------------------------------------------------------: | :-----: | :-----: | :-------: | :-------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| 内容理解 | [Text-Classifcation](models/contentunderstanding/classification/model.py) | ✓ | x | ✓ | x | / | | 内容理解 | [Text-Classifcation](models/contentunderstanding/classification/model.py) | ✓ | x | ✓ | x | [EMNLP 2014][Convolutional neural networks for sentence classication](https://www.aclweb.org/anthology/D14-1181.pdf) |
| 内容理解 | [TagSpace](models/contentunderstanding/tagspace/model.py) | ✓ | x | ✓ | x | / | | 内容理解 | [TagSpace](models/contentunderstanding/tagspace/model.py) | ✓ | x | ✓ | x | [EMNLP 2014][TagSpace: Semantic Embeddings from Hashtags](https://www.aclweb.org/anthology/D14-1194.pdf) |
| 匹配 | [DSSM](models/match/dssm/model.py) | ✓ | x | ✓ | x | / | | 匹配 | [DSSM](models/match/dssm/model.py) | ✓ | x | ✓ | x | [CIKM 2013][Learning Deep Structured Semantic Models for Web Search using Clickthrough Data](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/cikm2013_DSSM_fullversion.pdf) |
| 匹配 | [MultiView-Simnet](models/match/multiview-simnet/model.py) | ✓ | x | ✓ | x | / | | 匹配 | [MultiView-Simnet](models/match/multiview-simnet/model.py) | ✓ | x | ✓ | x | [WWW 2015][A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/frp1159-songA.pdf) |
| 召回 | [TDM](models/treebased/tdm/model.py) | ✓ | >=1.8.0 | ✓ | >=1.8.0 | [[KDD 2018](https://www.kdd.org/kdd2018/)][Learning Tree-based Deep Model for Recommender Systems](https://arxiv.org/pdf/1801.02294.pdf) | | 召回 | [TDM](models/treebased/tdm/model.py) | ✓ | >=1.8.0 | ✓ | >=1.8.0 | [KDD 2018][Learning Tree-based Deep Model for Recommender Systems](https://arxiv.org/pdf/1801.02294.pdf) |
| 召回 | [fasttext](models/recall/fasttext/model.py) | ✓ | x | x | x | / | | 召回 | [fasttext](models/recall/fasttext/model.py) | ✓ | x | x | x | [EACL 2017][Bag of Tricks for Efficient Text Classification](https://www.aclweb.org/anthology/E17-2068.pdf) |
| 召回 | [Word2Vec](models/recall/word2vec/model.py) | ✓ | x | ✓ | x | / | | 召回 | [Word2Vec](models/recall/word2vec/model.py) | ✓ | x | ✓ | x | [NIPS 2013][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) |
| 召回 | [SSR](models/recall/ssr/model.py) | ✓ | ✓ | ✓ | ✓ | / | | 召回 | [SSR](models/recall/ssr/model.py) | ✓ | ✓ | ✓ | ✓ | [SIGIR 2016][Multi-Rate Deep Learning for Temporal Recommendation](http://sonyis.me/paperpdf/spr209-song_sigir16.pdf) |
| 召回 | [Gru4Rec](models/recall/gru4rec/model.py) | ✓ | ✓ | ✓ | ✓ | / | | 召回 | [Gru4Rec](models/recall/gru4rec/model.py) | ✓ | ✓ | ✓ | ✓ | [2015][Session-based Recommendations with Recurrent Neural Networks](https://arxiv.org/abs/1511.06939) |
| 召回 | [Youtube_dnn](models/recall/youtube_dnn/model.py) | ✓ | ✓ | ✓ | ✓ | / | | 召回 | [Youtube_dnn](models/recall/youtube_dnn/model.py) | ✓ | ✓ | ✓ | ✓ | [RecSys 2016][Deep Neural Networks for YouTube Recommendations](https://static.googleusercontent.com/media/research.google.com/zh-CN//pubs/archive/45530.pdf) |
| 召回 | [NCF](models/recall/ncf/model.py) | ✓ | ✓ | ✓ | ✓ | / | | 召回 | [NCF](models/recall/ncf/model.py) | ✓ | ✓ | ✓ | ✓ | [WWW 2017][Neural Collaborative Filtering](https://arxiv.org/pdf/1708.05031.pdf) |
| 排序 | [Logistic Regression](models/rank/logistic_regression/model.py) | ✓ | x | ✓ | x | / | | 召回 | [GNN](models/recall/gnn/model.py) | ✓ | ✓ | ✓ | ✓ | [AAAI 2019][Session-based Recommendation with Graph Neural Networks](https://arxiv.org/abs/1811.00855) |
| 排序 | [Dnn](models/rank/dnn/model.py) | ✓ | ✓ | ✓ | ✓ | / | | 排序 | [Logistic Regression](models/rank/logistic_regression/model.py) | ✓ | x | ✓ | x | / |
| 排序 | [FM](models/rank/fm/model.py) | ✓ | ✓ | ✓ | ✓ | / | | 排序 | [Dnn](models/rank/dnn/model.py) | ✓ | ✓ | ✓ | ✓ | / |
| 排序 | [FFM](models/rank/ffm/model.py) | ✓ | x | ✓ | x | / | | 排序 | [FM](models/rank/fm/model.py) | ✓ | ✓ | ✓ | ✓ | / |
| 排序 | [Pnn](models/rank/pnn/model.py) | ✓ | x | ✓ | x | / | | 排序 | [FFM](models/rank/ffm/model.py) | ✓ | x | ✓ | x | / |
| 排序 | [DCN](models/rank/dcn/model.py) | ✓ | x | ✓ | x | / | | 排序 | [Pnn](models/rank/pnn/model.py) | ✓ | x | ✓ | x | / |
| 排序 | [NFM](models/rank/nfm/model.py) | ✓ | x | ✓ | x | / | | 排序 | [DCN](models/rank/dcn/model.py) | ✓ | x | ✓ | x | / |
| 排序 | [AFM](models/rank/afm/model.py) | ✓ | x | ✓ | x | / | | 排序 | [NFM](models/rank/nfm/model.py) | ✓ | x | ✓ | x | / |
| 排序 | [DeepFM](models/rank/deepfm/model.py) | ✓ | x | ✓ | x | / | | 排序 | [AFM](models/rank/afm/model.py) | ✓ | x | ✓ | x | / |
| 排序 | [xDeepFM](models/rank/xdeepfm/model.py) | ✓ | x | ✓ | x | / | | 排序 | [DeepFM](models/rank/deepfm/model.py) | ✓ | x | ✓ | x | / |
| 排序 | [DIN](models/rank/din/model.py) | ✓ | x | ✓ | x | / | | 排序 | [xDeepFM](models/rank/xdeepfm/model.py) | ✓ | x | ✓ | x | / |
| 排序 | [Wide&Deep](models/rank/wide_deep/model.py) | ✓ | x | ✓ | x | / | | 排序 | [DIN](models/rank/din/model.py) | ✓ | x | ✓ | x | / |
| 排序 | [FGCNN](models/rank/fgcnn/model.py) | ✓ | ✓ | ✓ | ✓ | / | | 排序 | [Wide&Deep](models/rank/wide_deep/model.py) | ✓ | x | ✓ | x | / |
| 多任务 | [ESMM](models/multitask/esmm/model.py) | ✓ | ✓ | ✓ | ✓ | / | | 排序 | [FGCNN](models/rank/fgcnn/model.py) | ✓ | ✓ | ✓ | ✓ | / |
| 多任务 | [MMOE](models/multitask/mmoe/model.py) | ✓ | ✓ | ✓ | ✓ | / | | 多任务 | [ESMM](models/multitask/esmm/model.py) | ✓ | ✓ | ✓ | ✓ | [SIGIR 2018][Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate](https://arxiv.org/abs/1804.07931) |
| 多任务 | [ShareBottom](models/multitask/share-bottom/model.py) | ✓ | ✓ | ✓ | ✓ | / | | 多任务 | [MMOE](models/multitask/mmoe/model.py) | ✓ | ✓ | ✓ | ✓ | [KDD 2018][Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts](https://dl.acm.org/doi/abs/10.1145/3219819.3220007) |
| 重排序 | [Listwise](models/rerank/listwise/model.py) | ✓ | x | ✓ | x | / | | 多任务 | [ShareBottom](models/multitask/share-bottom/model.py) | ✓ | ✓ | ✓ | ✓ | [1998][Multitask learning](http://reports-archive.adm.cs.cmu.edu/anon/1997/CMU-CS-97-203.pdf) |
| 重排序 | [Listwise](models/rerank/listwise/model.py) | ✓ | x | ✓ | x | [2019][Sequential Evaluation and Generation Framework for Combinatorial Recommender System](https://arxiv.org/pdf/1902.00245.pdf) |
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models/contentunderstanding/readme.md
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| 模型 | 简介 | 论文 | | 模型 | 简介 | 论文 |
| :------------------: | :--------------------: | :---------: | | :------------------: | :--------------------: | :---------: |
| TagSpace | 标签推荐 | [TagSpace: Semantic Embeddings from Hashtags (2014)](https://research.fb.com/publications/tagspace-semantic-embeddings-from-hashtags/) | | TagSpace | 标签推荐 | [EMNLP 2014][TagSpace: Semantic Embeddings from Hashtags](https://research.fb.com/publications/tagspace-semantic-embeddings-from-hashtags/) |
| Classification | 文本分类 | [Convolutional neural networks for sentence classication (2014)](https://www.aclweb.org/anthology/D14-1181.pdf) | | Classification | 文本分类 | [EMNLP 2014][Convolutional neural networks for sentence classication](https://www.aclweb.org/anthology/D14-1181.pdf) |
下面是每个模型的简介（注：图片引用自链接中的论文） 下面是每个模型的简介（注：图片引用自链接中的论文）
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models/match/readme.md
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| 模型 | 简介 | 论文 | | 模型 | 简介 | 论文 |
| :------------------: | :--------------------: | :---------: | | :------------------: | :--------------------: | :---------: |
| DSSM | Deep Structured Semantic Models | [Learning Deep Structured Semantic Models for Web Search using Clickthrough Data](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/cikm2013_DSSM_fullversion.pdf)(2013) | | DSSM | Deep Structured Semantic Models | [CIKM 2013][Learning Deep Structured Semantic Models for Web Search using Clickthrough Data](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/cikm2013_DSSM_fullversion.pdf) |
| MultiView-Simnet | Multi-view Simnet for Personalized recommendation | [A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/frp1159-songA.pdf)(2015) | | MultiView-Simnet | Multi-view Simnet for Personalized recommendation | [WWW 2015][A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/frp1159-songA.pdf) |
下面是每个模型的简介（注：图片引用自链接中的论文） 下面是每个模型的简介（注：图片引用自链接中的论文）
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models/multitask/readme.md
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### 多任务模型列表 ### 多任务模型列表
| 模型 | 简介 | 论文 | | 模型 | 简介 | 论文 |
| :------------------: | :--------------------: | :---------: | | :------------------: | :--------------------: | :--------- |
| Share-Bottom | share-bottom | [Multitask learning](http://reports-archive.adm.cs.cmu.edu/anon/1997/CMU-CS-97-203.pdf)(1998) | | Share-Bottom | share-bottom | [1998][Multitask learning](http://reports-archive.adm.cs.cmu.edu/anon/1997/CMU-CS-97-203.pdf) |
| ESMM | Entire Space Multi-Task Model | [Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate](https://arxiv.org/abs/1804.07931)(2018) | | ESMM | Entire Space Multi-Task Model | [SIGIR 2018][Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate](https://arxiv.org/abs/1804.07931) |
| MMoE | Multi-gate Mixture-of-Experts | [Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts](https://dl.acm.org/doi/abs/10.1145/3219819.3220007)(2018) | | MMOE | Multi-gate Mixture-of-Experts | [KDD 2018][Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts](https://dl.acm.org/doi/abs/10.1145/3219819.3220007) |
下面是每个模型的简介（注：图片引用自链接中的论文） 下面是每个模型的简介（注：图片引用自链接中的论文）
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models/recall/readme.md
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### 召回模型列表 ### 召回模型列表
| 模型 | 简介 | 论文 | | 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) | | Word2Vec | word2vector | [NIPS 2013][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) |
| GRU4REC | SR-GRU | [Session-based Recommendations with Recurrent Neural Networks](https://arxiv.org/abs/1511.06939)(2015) | | GRU4REC | SR-GRU | [2015][Session-based Recommendations with Recurrent Neural Networks](https://arxiv.org/abs/1511.06939) |
| 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) | | Youtube_DNN | Youtube_DNN | [RecSys 2016][Deep Neural Networks for YouTube Recommendations](https://static.googleusercontent.com/media/research.google.com/zh-CN//pubs/archive/45530.pdf) |
| SSR | Sequence Semantic Retrieval Model | [Multi-Rate Deep Learning for Temporal Recommendation](http://sonyis.me/paperpdf/spr209-song_sigir16.pdf)(2016) | | SSR | Sequence Semantic Retrieval Model | [SIGIR 2016][Multi-Rate Deep Learning for Temporal Recommendation](http://sonyis.me/paperpdf/spr209-song_sigir16.pdf) |
| NCF | Neural Collaborative Filtering | [Neural Collaborative Filtering](https://arxiv.org/pdf/1708.05031.pdf)(2017) | | NCF | Neural Collaborative Filtering | [WWW 2017][Neural Collaborative Filtering](https://arxiv.org/pdf/1708.05031.pdf) |
| GNN | SR-GNN | [Session-based Recommendation with Graph Neural Networks](https://arxiv.org/abs/1811.00855)(2018) | | GNN | SR-GNN | [AAAI 2019][Session-based Recommendation with Graph Neural Networks](https://arxiv.org/abs/1811.00855) |
| Fasttext | fasttext | [EACL 2017][Bag of Tricks for Efficient Text Classification](https://www.aclweb.org/anthology/E17-2068.pdf) |
下面是每个模型的简介（注：图片引用自链接中的论文） 下面是每个模型的简介（注：图片引用自链接中的论文）
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models/rerank/readme.md
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| 模型 | 简介 | 论文 | | 模型 | 简介 | 论文 |
| :------------------: | :--------------------: | :---------: | | :------------------: | :--------------------: | :---------: |
| Listwise | Listwise | [Sequential Evaluation and Generation Framework for Combinatorial Recommender System](https://arxiv.org/pdf/1902.00245.pdf)(2019) | | Listwise | Listwise | [2019][Sequential Evaluation and Generation Framework for Combinatorial Recommender System](https://arxiv.org/pdf/1902.00245.pdf) |
下面是每个模型的简介（注：图片引用自链接中的论文） 下面是每个模型的简介（注：图片引用自链接中的论文）
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models/treebased/README.md
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# Paddle-TDM # Paddle-TDM
本代码库提供了基于PaddlePaddle实现的TreeBased推荐搜索算法，主要包含以下组成：
TDM召回方法来源于阿里妈妈团队在`KDD2018`发表的论文[Learning Tree-based Deep Model for Recommender Systems](https://arxiv.org/pdf/1801.02294.pdf)，本示例代码提供了基于PaddlePaddle实现的TreeBased推荐搜索算法，主要包含以下组成：
- 基于fake数据集，适用于快速调试的paddle-tdm模型。主要用于理解paddle-tdm的设计原理，高效上手设计适合您的使用场景的模型。 - 基于fake数据集，适用于快速调试的paddle-tdm模型。主要用于理解paddle-tdm的设计原理，高效上手设计适合您的使用场景的模型。
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