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33 | 33 |
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34 | 34 | | 方向 | 模型 | 单机CPU | 单机GPU | 分布式CPU | 分布式GPU | 论文 | |
35 | 35 | | :------: | :-----------------------------------------------------------------------: | :-----: | :-----: | :-------: | :-------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | |
36 | | - | 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) | |
37 | | - | 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) | |
38 | | - | 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) | |
39 | | - | 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) | |
| 36 | + | 内容理解 | [Text-Classifcation](models/contentunderstanding/classification/model.py) | ✓ | ✓ | ✓ | x | [EMNLP 2014][Convolutional neural networks for sentence classication](https://www.aclweb.org/anthology/D14-1181.pdf) | |
| 37 | + | 内容理解 | [TagSpace](models/contentunderstanding/tagspace/model.py) | ✓ | ✓ | ✓ | x | [EMNLP 2014][TagSpace: Semantic Embeddings from Hashtags](https://www.aclweb.org/anthology/D14-1194.pdf) | |
| 38 | + | 匹配 | [DSSM](models/match/dssm/model.py) | ✓ | ✓ | ✓ | 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) | |
| 39 | + | 匹配 | [MultiView-Simnet](models/match/multiview-simnet/model.py) | ✓ | ✓ | ✓ | 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) | |
40 | 40 | | 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) | |
41 | | - | 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) | |
42 | | - | 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) | |
| 41 | + | 召回 | [fasttext](models/recall/fasttext/model.py) | ✓ | ✓ | x | x | [EACL 2017][Bag of Tricks for Efficient Text Classification](https://www.aclweb.org/anthology/E17-2068.pdf) | |
| 42 | + | 召回 | [Word2Vec](models/recall/word2vec/model.py) | ✓ | ✓ | ✓ | 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) | |
43 | 43 | | 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) | |
44 | 44 | | 召回 | [Gru4Rec](models/recall/gru4rec/model.py) | ✓ | ✓ | ✓ | ✓ | [2015][Session-based Recommendations with Recurrent Neural Networks](https://arxiv.org/abs/1511.06939) | |
45 | 45 | | 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) | |
46 | 46 | | 召回 | [NCF](models/recall/ncf/model.py) | ✓ | ✓ | ✓ | ✓ | [WWW 2017][Neural Collaborative Filtering](https://arxiv.org/pdf/1708.05031.pdf) | |
47 | 47 | | 召回 | [GNN](models/recall/gnn/model.py) | ✓ | ✓ | ✓ | ✓ | [AAAI 2019][Session-based Recommendation with Graph Neural Networks](https://arxiv.org/abs/1811.00855) | |
48 | | - | 排序 | [Logistic Regression](models/rank/logistic_regression/model.py) | ✓ | x | ✓ | x | / | |
| 48 | + | 排序 | [Logistic Regression](models/rank/logistic_regression/model.py) | ✓ | ✓ | ✓ | x | / | |
49 | 49 | | 排序 | [Dnn](models/rank/dnn/model.py) | ✓ | ✓ | ✓ | ✓ | / | |
50 | 50 | | 排序 | [FM](models/rank/fm/model.py) | ✓ | ✓ | ✓ | ✓ | / | |
51 | | - | 排序 | [FFM](models/rank/ffm/model.py) | ✓ | x | ✓ | x | / | |
52 | | - | 排序 | [Pnn](models/rank/pnn/model.py) | ✓ | x | ✓ | x | / | |
53 | | - | 排序 | [DCN](models/rank/dcn/model.py) | ✓ | x | ✓ | x | / | |
54 | | - | 排序 | [NFM](models/rank/nfm/model.py) | ✓ | x | ✓ | x | / | |
55 | | - | 排序 | [AFM](models/rank/afm/model.py) | ✓ | x | ✓ | x | / | |
56 | | - | 排序 | [DeepFM](models/rank/deepfm/model.py) | ✓ | x | ✓ | x | / | |
57 | | - | 排序 | [xDeepFM](models/rank/xdeepfm/model.py) | ✓ | x | ✓ | x | / | |
58 | | - | 排序 | [DIN](models/rank/din/model.py) | ✓ | x | ✓ | x | / | |
59 | | - | 排序 | [Wide&Deep](models/rank/wide_deep/model.py) | ✓ | x | ✓ | x | / | |
| 51 | + | 排序 | [FFM](models/rank/ffm/model.py) | ✓ | ✓ | ✓ | x | / | |
| 52 | + | 排序 | [Pnn](models/rank/pnn/model.py) | >=2.0 | >=2.0 | >=2.0 | >=2.0 | / | |
| 53 | + | 排序 | [DCN](models/rank/dcn/model.py) | ✓ | ✓ | ✓ | x | / | |
| 54 | + | 排序 | [NFM](models/rank/nfm/model.py) | ✓ | ✓ | ✓ | x | / | |
| 55 | + | 排序 | [AFM](models/rank/afm/model.py) | ✓ | ✓ | ✓ | x | / | |
| 56 | + | 排序 | [DeepFM](models/rank/deepfm/model.py) | ✓ | ✓ | ✓ | x | / | |
| 57 | + | 排序 | [xDeepFM](models/rank/xdeepfm/model.py) | ✓ | ✓ | ✓ | x | / | |
| 58 | + | 排序 | [DIN](models/rank/din/model.py) | ✓ | ✓ | ✓ | x | / | |
| 59 | + | 排序 | [Wide&Deep](models/rank/wide_deep/model.py) | ✓ | ✓ | ✓ | x | / | |
60 | 60 | | 排序 | [FGCNN](models/rank/fgcnn/model.py) | ✓ | ✓ | ✓ | ✓ | / | |
61 | 61 | | 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) | |
62 | 62 | | 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) | |
63 | 63 | | 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) | |
64 | | - | 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) | |
| 64 | + | 重排序 | [Listwise](models/rerank/listwise/model.py) | ✓ | ✓ | ✓ | x | [2019][Sequential Evaluation and Generation Framework for Combinatorial Recommender System](https://arxiv.org/pdf/1902.00245.pdf) | |
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