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Merge pull request #703 from wangzhen38/add_flen
add flen
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‎README_CN.md‎

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| 召回 | [NCF](models/recall/ncf/)([文档](https://paddlerec.readthedocs.io/en/latest/models/recall/ncf.html)) | [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3240152) | ✓ | ✓ | >=2.1.0 | [WWW 2017][Neural Collaborative Filtering](https://arxiv.org/pdf/1708.05031.pdf) |
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| 召回 | [TiSAS](models/recall/tisas/) | - | ✓ | ✓ | >=2.1.0 | [WSDM 2020][Time Interval Aware Self-Attention for Sequential Recommendation](https://cseweb.ucsd.edu/~jmcauley/pdfs/wsdm20b.pdf) |
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| 召回 | [ENSFM](models/recall/ensfm/) | - | ✓ | ✓ | >=2.1.0 | [IW3C2 2020][Eicient Non-Sampling Factorization Machines for Optimal Context-Aware Recommendation](http://www.thuir.cn/group/~mzhang/publications/TheWebConf2020-Chenchong.pdf) |
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| 召回 | [MHCN](models/recall/mhcn/) | - | ✓ | ✓ | >=2.1.0 | [WWW 2021][Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation](https://arxiv.org/pdf/2101.06448v3.pdf) |
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| 召回 | [GNN](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5/models/recall/gnn/) | - | ✓ | ✓ | [1.8.5](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5) | [AAAI 2019][Session-based Recommendation with Graph Neural Networks](https://arxiv.org/abs/1811.00855) |
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| 召回 | [RALM](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5/models/recall/look-alike_recall/) | - | ✓ | ✓ | [1.8.5](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5) | [KDD 2019][Real-time Attention Based Look-alike Model for Recommender System](https://arxiv.org/pdf/1906.05022.pdf) |
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| 排序 | [Logistic Regression](models/rank/logistic_regression/)([文档](https://paddlerec.readthedocs.io/en/latest/models/rank/logistic_regression.html)) | [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3240481) | ✓ | x | >=2.1.0 | / |
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| 排序 | [Wide&Deep](models/rank/wide_deep/)([文档](https://paddlerec.readthedocs.io/en/latest/models/rank/wide_deep.html)) | [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3238421) | ✓ | x | >=2.1.0 | [DLRS 2016][Wide & Deep Learning for Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/2988450.2988454) |
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| 排序 | [FGCNN](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5/models/rank/fgcnn/) | - | ✓ | ✓ | [1.8.5](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5) | [WWW 2019][Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1904.04447.pdf) |
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| 排序 | [Fibinet](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5/models/rank/fibinet/) | - | ✓ | ✓ | [1.8.5](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5) | [RecSys19][FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction]( https://arxiv.org/pdf/1905.09433.pdf) |
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| 排序 | [Flen](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5/models/rank/flen/) | - | ✓ | ✓ | [1.8.5](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5) | [2019][FLEN: Leveraging Field for Scalable CTR Prediction]( https://arxiv.org/pdf/1911.04690.pdf) |
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| 排序 | [FLEN](models/rank/flen/) | - | ✓ | ✓ | >=2.1.0 | [2019][FLEN: Leveraging Field for Scalable CTR Prediction]( https://arxiv.org/pdf/1911.04690.pdf) |
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| 排序 | [DeepRec](models/rank/deeprec/) | - | ✓ | ✓ | >=2.1.0 | [2017][Training Deep AutoEncoders for Collaborative Filtering](https://arxiv.org/pdf/1708.01715v3.pdf) |
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| 排序 | [AutoFIS](models/rank/autofis/) | - | ✓ | ✓ | >=2.1.0 | [KDD 2020][AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction](https://arxiv.org/pdf/2003.11235v3.pdf) | | - | ✓ | ✓ | [1.8.5](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5) | [2019][FLEN: Leveraging Field for Scalable CTR Prediction]( https://arxiv.org/pdf/1911.04690.pdf) |
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| 排序 | [AutoFIS](models/rank/autofis/) | - | ✓ | ✓ | >=2.1.0 | [KDD 2020][AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction](https://arxiv.org/pdf/2003.11235v3.pdf)
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| 多任务 | [PLE](models/multitask/ple/)([文档](https://paddlerec.readthedocs.io/en/latest/models/multitask/ple.html)) | [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3238938) | ✓ | ✓ | >=2.1.0 | [RecSys 2020][Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations](https://dl.acm.org/doi/abs/10.1145/3383313.3412236) |
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| 多任务 | [ESMM](models/multitask/esmm/)([文档](https://paddlerec.readthedocs.io/en/latest/models/multitask/esmm.html)) | [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3238583) | ✓ | ✓ | >=2.1.0 | [SIGIR 2018][Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate](https://arxiv.org/abs/1804.07931) |
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| 多任务 | [MMOE](models/multitask/mmoe/)([文档](https://paddlerec.readthedocs.io/en/latest/models/multitask/mmoe.html)) | [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3238934) | ✓ | ✓ | >=2.1.0 | [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) |

‎README_EN.md‎

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| Recall | [NCF](models/recall/ncf/)<br>([doc](https://paddlerec.readthedocs.io/en/latest/models/recall/ncf.html)) | [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3240152) | ✓ | ✓ | >=2.1.0 | [WWW 2017][Neural Collaborative Filtering](https://arxiv.org/pdf/1708.05031.pdf) |
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| Recall | [TiSAS](models/recall/tisas/) | - | ✓ | ✓ | >=2.1.0 | [WSDM 2020][Time Interval Aware Self-Attention for Sequential Recommendation](https://cseweb.ucsd.edu/~jmcauley/pdfs/wsdm20b.pdf) |
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| Recall | [ENSFM](models/recall/ensfm/) | - | ✓ | ✓ | >=2.1.0 | [IW3C2 2020][Eicient Non-Sampling Factorization Machines for Optimal Context-Aware Recommendation](http://www.thuir.cn/group/~mzhang/publications/TheWebConf2020-Chenchong.pdf) |
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| Recall | [MHCN](models/recall/mhcn/) | - | ✓ | ✓ | >=2.1.0 | [WWW 2021][Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation](https://arxiv.org/pdf/2101.06448v3.pdf) |
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| Recall | [GNN](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5/models/recall/gnn/) | - | ✓ | ✓ | [1.8.5](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5) | [AAAI 2019][Session-based Recommendation with Graph Neural Networks](https://arxiv.org/abs/1811.00855) |
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| Recall | [RALM](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5/models/recall/look-alike_recall/) | - | ✓ | ✓ | [1.8.5](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5) | [KDD 2019][Real-time Attention Based Look-alike Model for Recommender System](https://arxiv.org/pdf/1906.05022.pdf) |
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| Rank | [Logistic Regression](models/rank/logistic_regression/)<br>([doc](https://paddlerec.readthedocs.io/en/latest/models/rank/logistic_regression.html)) | [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3240481) | ✓ | x | >=2.1.0 | / |
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| Rank | [Wide&Deep](models/rank/wide_deep/)<br>([doc](https://paddlerec.readthedocs.io/en/latest/models/rank/wide_deep.html)) | [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3238421) | ✓ | x | >=2.1.0 | [DLRS 2016][Wide & Deep Learning for Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/2988450.2988454) |
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| Rank | [FGCNN](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5/models/rank/fgcnn/) | - | ✓ | ✓ | [1.8.5](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5) | [WWW 2019][Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1904.04447.pdf) |
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| Rank | [Fibinet](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5/models/rank/fibinet/) | - | ✓ | ✓ | [1.8.5](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5) | [RecSys19][FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction]( https://arxiv.org/pdf/1905.09433.pdf) |
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| Rank | [Flen](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5/models/rank/flen/) | - | ✓ | ✓ | [1.8.5](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5) | [2019][FLEN: Leveraging Field for Scalable CTR Prediction]( https://arxiv.org/pdf/1911.04690.pdf) |
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| Rank | [FLEN](models/rank/flen/) | - | ✓ | ✓ | >=2.1.0 | [2019][FLEN: Leveraging Field for Scalable CTR Prediction]( https://arxiv.org/pdf/1911.04690.pdf) |
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| Rank | [DeepRec](models/rank/deeprec/) | - | ✓ | ✓ | >=2.1.0 | [2017][Training Deep AutoEncoders for Collaborative Filtering](https://arxiv.org/pdf/1708.01715v3.pdf) |
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| Rank | [AutoFIS](models/rank/autofis/) | - | ✓ | ✓ | >=2.1.0 | [KDD 2020][AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction](https://arxiv.org/pdf/2003.11235v3.pdf) |
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| Multi-Task | [PLE](models/multitask/ple/)<br>([doc](https://paddlerec.readthedocs.io/en/latest/models/multitask/ple.html)) | [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3238938) | ✓ | ✓ | >=2.1.0 | [RecSys 2020][Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations](https://dl.acm.org/doi/abs/10.1145/3383313.3412236) |

‎contributor.md‎

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| [BERT4REC](models/rank/bert4rec/) | [jinweiluo](https://github.com/jinweiluo) | https://github.com/PaddlePaddle/PaddleRec/pull/624 | 论文复现赛第四期 |
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| [FAT_DeepFFM](models/rank/fat_deepffm/) | [LinJayan](https://github.com/LinJayan) | https://github.com/PaddlePaddle/PaddleRec/pull/651 | 论文复现赛第四期 |
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| [DeepRec](models/rank/deeprec/) | [chenjiyan2001](https://github.com/chenjiyan2001) | https://github.com/PaddlePaddle/PaddleRec/pull/647 | 论文复现赛第五期 |
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| [ENSFM](models/recal/ensfm/) | [renmada](https://github.com/renmada) | https://github.com/PaddlePaddle/PaddleRec/pull/618 | 论文复现赛第五期 |
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| [TiSAS](models/recal/tisas/) | [renmada](https://github.com/renmada) | https://github.com/PaddlePaddle/PaddleRec/pull/625 | 论文复现赛第五期 |
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| [ENSFM](models/recall/ensfm/) | [renmada](https://github.com/renmada) | https://github.com/PaddlePaddle/PaddleRec/pull/618 | 论文复现赛第五期 |
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| [TiSAS](models/recall/tisas/) | [renmada](https://github.com/renmada) | https://github.com/PaddlePaddle/PaddleRec/pull/625 | 论文复现赛第五期 |
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| [AutoFIS](models/rank/autofis/) | [renmada](https://github.com/renmada) | https://github.com/PaddlePaddle/PaddleRec/pull/660 | 论文复现赛第五期 |
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| [Dselect_K](models/multitask/dselect_k/) | [Andy1314Chen](https://github.com/Andy1314Chen) | https://github.com/PaddlePaddle/PaddleRec/pull/671 | 论文复现赛第五期 |
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| [MIND](models/recall/mind/) | [duyiqi17 ](https://github.com/duyiqi17) | https://github.com/PaddlePaddle/PaddleRec/pull/398 | 其他 |
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| [FLEN](models/rank/flen/) | [LinJayan](https://github.com/LinJayan) | https://github.com/PaddlePaddle/PaddleRec/pull/685 | 论文复现赛第五期 |
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</div>

‎datasets/Avazu_flen/data_config.yaml‎

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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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runner:
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raw_file_dir: "path" # raw_data dir
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raw_filled_file_dir: "./raw_data" # raw_data_filled dir
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train_data_dir: "./train_data_full" # train datasets
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test_data_dir: "./test_data_full" # test datasets
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rebuild_feature_map: True # False use feature_map_cache
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min_threshold: 4
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feature_map_cache: '.feature_map'
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