From e0d38aeb8754af8e3564be97540495e7ec7ef8cb Mon Sep 17 00:00:00 2001 From: yaoxuefeng Date: Mon, 15 Jun 2020 14:12:06 +0800 Subject: [PATCH] update readme of rank models (#87) --- README.md | 28 +++++++++++++++------------- models/rank/readme.md | 2 ++ 2 files changed, 17 insertions(+), 13 deletions(-) diff --git a/README.md b/README.md index fe194498..4b2e9110 100644 --- a/README.md +++ b/README.md @@ -45,19 +45,21 @@ | 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 | / | - | 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 | / | - | 排序 | [Pnn](models/rank/pnn/model.py) | >=2.0 | >=2.0 | >=2.0 | >=2.0 | / | - | 排序 | [DCN](models/rank/dcn/model.py) | ✓ | ✓ | ✓ | x | / | - | 排序 | [NFM](models/rank/nfm/model.py) | ✓ | ✓ | ✓ | x | / | - | 排序 | [AFM](models/rank/afm/model.py) | ✓ | ✓ | ✓ | x | / | - | 排序 | [DeepFM](models/rank/deepfm/model.py) | ✓ | ✓ | ✓ | x | / | - | 排序 | [xDeepFM](models/rank/xdeepfm/model.py) | ✓ | ✓ | ✓ | x | / | - | 排序 | [DIN](models/rank/din/model.py) | ✓ | ✓ | ✓ | x | / | - | 排序 | [Wide&Deep](models/rank/wide_deep/model.py) | ✓ | ✓ | ✓ | x | / | - | 排序 | [FGCNN](models/rank/fgcnn/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) | ✓ | x | ✓ | x | [IEEE Data Mining 2010][Factorization machines](https://analyticsconsultores.com.mx/wp-content/uploads/2019/03/Factorization-Machines-Steffen-Rendle-Osaka-University-2010.pdf) | + | 排序 | [FFM](models/rank/ffm/model.py) | ✓ | x | ✓ | x | [RECSYS 2016][Field-aware Factorization Machines for CTR Prediction](https://dl.acm.org/doi/pdf/10.1145/2959100.2959134) | + | 排序 | [FNN](models/rank/fnn/model.py) | ✓ | x | ✓ | x | [ECIR 2016][Deep Learning over Multi-field Categorical Data](https://arxiv.org/pdf/1601.02376.pdf) | + | 排序 | [Deep Crossing](models/rank/deep_crossing/model.py) | ✓ | x | ✓ | x | [ACM 2016][Deep Crossing: Web-Scale Modeling without Manually Crafted Combinatorial Features](https://www.kdd.org/kdd2016/papers/files/adf0975-shanA.pdf) | + | 排序 | [Pnn](models/rank/pnn/model.py) | ✓ | x | ✓ | x | [ICDM 2016][Product-based Neural Networks for User Response Prediction](https://arxiv.org/pdf/1611.00144.pdf) | + | 排序 | [DCN](models/rank/dcn/model.py) | ✓ | x | ✓ | x | [KDD 2017][Deep & Cross Network for Ad Click Predictions](https://dl.acm.org/doi/pdf/10.1145/3124749.3124754) | + | 排序 | [NFM](models/rank/nfm/model.py) | ✓ | x | ✓ | x | [SIGIR 2017][Neural Factorization Machines for Sparse Predictive Analytics](https://dl.acm.org/doi/pdf/10.1145/3077136.3080777) | + | 排序 | [AFM](models/rank/afm/model.py) | ✓ | x | ✓ | x | [IJCAI 2017][Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks](https://arxiv.org/pdf/1708.04617.pdf) | + | 排序 | [DeepFM](models/rank/deepfm/model.py) | ✓ | x | ✓ | x | [IJCAI 2017][DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](https://arxiv.org/pdf/1703.04247.pdf) | + | 排序 | [xDeepFM](models/rank/xdeepfm/model.py) | ✓ | x | ✓ | x | [KDD 2018][xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3219819.3220023) | + | 排序 | [DIN](models/rank/din/model.py) | ✓ | x | ✓ | x | [KDD 2018][Deep Interest Network for Click-Through Rate Prediction](https://dl.acm.org/doi/pdf/10.1145/3219819.3219823) | + | 排序 | [Wide&Deep](models/rank/wide_deep/model.py) | ✓ | x | ✓ | x | [DLRS 2016][Wide & Deep Learning for Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/2988450.2988454) | + | 排序 | [FGCNN](models/rank/fgcnn/model.py) | ✓ | ✓ | ✓ | ✓ |[WWW 2019][Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1904.04447.pdf)| | 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) | diff --git a/models/rank/readme.md b/models/rank/readme.md index 76892c99..d94f359f 100755 --- a/models/rank/readme.md +++ b/models/rank/readme.md @@ -26,6 +26,8 @@ | Logistic Regression | 逻辑回归 | -- | | FM | 因子分解机 | [Factorization Machine](https://ieeexplore.ieee.org/abstract/document/5694074)(2010) | | FFM | Field-Aware FM | [Field-aware Factorization Machines for CTR Prediction](https://dl.acm.org/doi/pdf/10.1145/2959100.2959134)(2016) | +| FNN | Factorisation-Machine Supported Neural Networks | [Deep Learning over Multi-field Categorical Data](https://arxiv.org/pdf/1601.02376.pdf)(2016) | +| Deep Crossing | Deep Crossing | [Deep Crossing: Web-Scale Modeling without Manually Crafted Combinatorial Features](https://www.kdd.org/kdd2016/papers/files/adf0975-shanA.pdf)(2016) | | PNN | Product Network | [Product-based Neural Networks for User Response Prediction](https://arxiv.org/pdf/1611.00144.pdf)(2016) | | wide&deep | Deep + wide(LR) | [Wide & Deep Learning for Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/2988450.2988454)(2016) | | DeepFM | DeepFM | [DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](https://arxiv.org/pdf/1703.04247.pdf)(2017) | -- GitLab