diff --git a/README.md b/README.md index 94a37e68da82f99dc38d3910ceb1d9bb494e7667..5c9ed3af276e8cbf6666b0870b119a550c63121f 100644 --- a/README.md +++ b/README.md @@ -31,36 +31,37 @@ - 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 | - | :------: | :-----------------------------------------------------------------------: | :-----: | :-----: | :-------: | :-------: | :--------------------------------------------------------------------------------------------------------------------------------------: | - | 内容理解 | [Text-Classifcation](models/contentunderstanding/classification/model.py) | ✓ | x | ✓ | x | / | - | 内容理解 | [TagSpace](models/contentunderstanding/tagspace/model.py) | ✓ | x | ✓ | x | / | - | 匹配 | [DSSM](models/match/dssm/model.py) | ✓ | x | ✓ | x | / | - | 匹配 | [MultiView-Simnet](models/match/multiview-simnet/model.py) | ✓ | x | ✓ | x | / | - | 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 | [fasttext](models/recall/fasttext/model.py) | ✓ | x | x | x | / | - | 召回 | [Word2Vec](models/recall/word2vec/model.py) | ✓ | x | ✓ | x | / | - | 召回 | [SSR](models/recall/ssr/model.py) | ✓ | ✓ | ✓ | ✓ | / | - | 召回 | [Gru4Rec](models/recall/gru4rec/model.py) | ✓ | ✓ | ✓ | ✓ | / | - | 召回 | [Youtube_dnn](models/recall/youtube_dnn/model.py) | ✓ | ✓ | ✓ | ✓ | / | - | 召回 | [NCF](models/recall/ncf/model.py) | ✓ | ✓ | ✓ | ✓ | / | - | 排序 | [Logistic Regression](models/rank/logistic_regression/model.py) | ✓ | x | ✓ | x | / | - | 排序 | [Dnn](models/rank/dnn/model.py) | ✓ | ✓ | ✓ | ✓ | / | - | 排序 | [FM](models/rank/fm/model.py) | ✓ | ✓ | ✓ | ✓ | / | - | 排序 | [FFM](models/rank/ffm/model.py) | ✓ | x | ✓ | x | / | - | 排序 | [Pnn](models/rank/pnn/model.py) | ✓ | x | ✓ | x | / | - | 排序 | [DCN](models/rank/dcn/model.py) | ✓ | x | ✓ | x | / | - | 排序 | [NFM](models/rank/nfm/model.py) | ✓ | x | ✓ | x | / | - | 排序 | [AFM](models/rank/afm/model.py) | ✓ | x | ✓ | x | / | - | 排序 | [DeepFM](models/rank/deepfm/model.py) | ✓ | x | ✓ | x | / | - | 排序 | [xDeepFM](models/rank/xdeepfm/model.py) | ✓ | x | ✓ | x | / | - | 排序 | [DIN](models/rank/din/model.py) | ✓ | x | ✓ | x | / | - | 排序 | [Wide&Deep](models/rank/wide_deep/model.py) | ✓ | x | ✓ | x | / | - | 排序 | [FGCNN](models/rank/fgcnn/model.py) | ✓ | ✓ | ✓ | ✓ | / | - | 多任务 | [ESMM](models/multitask/esmm/model.py) | ✓ | ✓ | ✓ | ✓ | / | - | 多任务 | [MMOE](models/multitask/mmoe/model.py) | ✓ | ✓ | ✓ | ✓ | / | - | 多任务 | [ShareBottom](models/multitask/share-bottom/model.py) | ✓ | ✓ | ✓ | ✓ | / | - | 重排序 | [Listwise](models/rerank/listwise/model.py) | ✓ | x | ✓ | x | / | + | 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 | [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 | [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 | [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 | [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][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 | [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 | [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) | ✓ | ✓ | ✓ | ✓ | [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) | ✓ | ✓ | ✓ | ✓ | [2015][Session-based Recommendations with Recurrent Neural Networks](https://arxiv.org/abs/1511.06939) | + | 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) | ✓ | ✓ | ✓ | ✓ | [WWW 2017][Neural Collaborative Filtering](https://arxiv.org/pdf/1708.05031.pdf) | + | 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 | [Logistic Regression](models/rank/logistic_regression/model.py) | ✓ | x | ✓ | x | / | + | 排序 | [Dnn](models/rank/dnn/model.py) | ✓ | ✓ | ✓ | ✓ | / | + | 排序 | [FM](models/rank/fm/model.py) | ✓ | ✓ | ✓ | ✓ | / | + | 排序 | [FFM](models/rank/ffm/model.py) | ✓ | x | ✓ | x | / | + | 排序 | [Pnn](models/rank/pnn/model.py) | ✓ | x | ✓ | x | / | + | 排序 | [DCN](models/rank/dcn/model.py) | ✓ | x | ✓ | x | / | + | 排序 | [NFM](models/rank/nfm/model.py) | ✓ | x | ✓ | x | / | + | 排序 | [AFM](models/rank/afm/model.py) | ✓ | x | ✓ | x | / | + | 排序 | [DeepFM](models/rank/deepfm/model.py) | ✓ | x | ✓ | x | / | + | 排序 | [xDeepFM](models/rank/xdeepfm/model.py) | ✓ | x | ✓ | x | / | + | 排序 | [DIN](models/rank/din/model.py) | ✓ | x | ✓ | x | / | + | 排序 | [Wide&Deep](models/rank/wide_deep/model.py) | ✓ | x | ✓ | x | / | + | 排序 | [FGCNN](models/rank/fgcnn/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 | [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) | + | 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) | diff --git a/models/contentunderstanding/readme.md b/models/contentunderstanding/readme.md index 217d7124d7cdb481ca7aacb418e36148508e42b8..56ec74a3e593760fa8f8b11d3521d612f894dd59 100644 --- a/models/contentunderstanding/readme.md +++ b/models/contentunderstanding/readme.md @@ -22,8 +22,8 @@ | 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/) | -| Classification | 文本分类 | [Convolutional neural networks for sentence classication (2014)](https://www.aclweb.org/anthology/D14-1181.pdf) | +| 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 | [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） diff --git a/models/match/readme.md b/models/match/readme.md index 5599dfbfcb7638e50d916b7014ed742307dc9717..f8f000880836ac604bf208902da57fb0281f19bb 100755 --- a/models/match/readme.md +++ b/models/match/readme.md @@ -16,8 +16,8 @@ | 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) | -| 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) | +| 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 | [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） diff --git a/models/multitask/readme.md b/models/multitask/readme.md index 07a6c01d77b72ed47153c3fad92521429a4769a2..4a377da6d1f9c081beecb1494ae8a680bc30a523 100755 --- a/models/multitask/readme.md +++ b/models/multitask/readme.md @@ -19,10 +19,10 @@ ### 多任务模型列表 | 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) | -| 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) | -| 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) | +| :------------------: | :--------------------: | :--------- | +| 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 | [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 | [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） diff --git a/models/recall/readme.md b/models/recall/readme.md index c51693fdb11613984c1abbf3a48a5306b40e8b61..bb0539b268ce41e41c29fa8f5cf6d9bfdef00a40 100755 --- a/models/recall/readme.md +++ b/models/recall/readme.md @@ -17,13 +17,14 @@ ### 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) | +| :------------------: | :--------------------: | :--------- | +| 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 | [2015][Session-based Recommendations with Recurrent Neural Networks](https://arxiv.org/abs/1511.06939) | +| 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 | [SIGIR 2016][Multi-Rate Deep Learning for Temporal Recommendation](http://sonyis.me/paperpdf/spr209-song_sigir16.pdf) | +| NCF | Neural Collaborative Filtering | [WWW 2017][Neural Collaborative Filtering](https://arxiv.org/pdf/1708.05031.pdf) | +| 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） diff --git a/models/rerank/readme.md b/models/rerank/readme.md index 6f698daf9f9a7529abcb8d18010965988838a940..f889a58e6c2111e9c81a8d5ce7c1c607bafa0d0d 100755 --- a/models/rerank/readme.md +++ b/models/rerank/readme.md @@ -15,7 +15,7 @@ | 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） diff --git a/models/treebased/README.md b/models/treebased/README.md index 3ceb13b62eba8127aa0394397d141b2abe343a32..4aa4f88762a3d1a78acb8fb7445c227d3556ef5b 100644 --- a/models/treebased/README.md +++ b/models/treebased/README.md @@ -1,5 +1,6 @@ # 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。