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Merge pull request #774 from renmada/iprec
Add iprec model
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‎README_CN.md‎

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| 排序 | [DCN_V2](models/rank/dcn_v2/) | - | ✓ | ✓ | >=2.1.0 | [WWW 2021][DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems](https://arxiv.org/pdf/2008.13535v2.pdf)|
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| 排序 | [DSIN](models/rank/dsin/) | - | ✓ | ✓ | >=2.1.0 | [IJCAI 2019][Deep Session Interest Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1905.06482v1.pdf) |
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| 排序 | [SIGN](models/rank/sign/)([文档](https://paddl7erec.readthedocs.io/en/latest/models/rank/sign.html)) | [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3869111) | ✓ | ✓ | >=2.1.0 | [AAAI 2021][Detecting Beneficial Feature Interactions for Recommender Systems](https://arxiv.org/pdf/2008.00404v6.pdf) |
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| 多任务 | [AITM](models/rank/aitm/) | - | ✓ | ✓ | >=2.1.0 | [KDD 2021][Modeling the Sequential Dependence among Audience Multi-step Conversions with Multi-task Learning in Targeted Display Advertising](https://arxiv.org/pdf/2105.08489v2.pdf) |
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| 排序 | [IPRec](models/rank/iprec/)([文档](https://paddl7erec.readthedocs.io/en/latest/models/rank/iprec.html)) | - | ✓ | ✓ | >=2.1.0 | [SIGIR 2021][Package Recommendation with Intra- and Inter-Package Attention Networks](http://nlp.csai.tsinghua.edu.cn/~xrb/publications/SIGIR-21_IPRec.pdf) | 多任务 | [AITM](models/rank/aitm/) | - | ✓ | ✓ | >=2.1.0 | [KDD 2021][Modeling the Sequential Dependence among Audience Multi-step Conversions with Multi-task Learning in Targeted Display Advertising](https://arxiv.org/pdf/2105.08489v2.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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| Rank | [DCN_V2](models/rank/dcn_v2/) | - | ✓ | ✓ | >=2.1.0 | [WWW 2021][DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems](https://arxiv.org/pdf/2008.13535v2.pdf)|
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| Rank | [DSIN](models/rank/dsin/) | - | ✓ | ✓ | >=2.1.0 | [IJCAI 2019][Deep Session Interest Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1905.06482v1.pdf) |
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| Rank | [SIGN](models/rank/sign/)([doc](https://paddlerec.readthedocs.io/en/latest/models/rank/sign.html)) | [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3869111) | ✓ | ✓ | >=2.1.0 | [AAAI 2021][Detecting Beneficial Feature Interactions for Recommender Systems](https://arxiv.org/pdf/2008.00404v6.pdf) |
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| Rank | [IPRec](models/rank/iprec/)([doc](https://paddl7erec.readthedocs.io/en/latest/models/rank/iprec.html)) | - | ✓ | ✓ | >=2.1.0 | [SIGIR 2021][Package Recommendation with Intra- and Inter-Package Attention Networks](http://nlp.csai.tsinghua.edu.cn/~xrb/publications/SIGIR-21_IPRec.pdf) |
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| Multi-Task | [AITM](models/rank/aitm/) | - | ✓ | ✓ | >=2.1.0 | [KDD 2021][Modeling the Sequential Dependence among Audience Multi-step Conversions with Multi-task Learning in Targeted Display Advertising](https://arxiv.org/pdf/2105.08489v2.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) |
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| Multi-Task | [ESMM](models/multitask/esmm/)<br>([doc](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) |

‎datasets/iprec/run.sh‎

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mkdir whole_data
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mkdir whole_data/train && mkdir whole_data/test
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wget https://paddlerec.bj.bcebos.com/datasets/IPREC/3_days.zip
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unzip 3_days.zip
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mv train.jsonl whole_data/train
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mv test.jsonl whole_data/test

‎doc/imgs/iprec.png‎

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‎doc/source/models/rank/iprec.md‎

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# iprec模型
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代码请参考：[AITM](https://github.com/PaddlePaddle/PaddleRec/tree/master/models/rank/iprec)
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如果我们的代码对您有用，还请点个star啊~
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注：在阅读该示例前，建议您先了解以下内容：
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[paddlerec入门教程](https://github.com/PaddlePaddle/PaddleRec/blob/master/README.md)
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## 内容
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- [模型简介](#模型简介)
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- [数据准备](#数据准备)
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- [运行环境](#运行环境)
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- [快速开始](#快速开始)
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- [模型组网](#模型组网)
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- [效果复现](#效果复现)
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- [进阶使用](#进阶使用)
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- [FAQ](#FAQ)
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## 模型简介
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Package Recommendation with Intra- and Inter-Package Attention Networks 《利用“包内”和“包间”注意力网络的包推荐》。
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随着移动互联网中在线社交网络的蓬勃发展，我们提出了一个新颖的社交推荐场景，名为Package Recommendation。在这种场景中，用户不再被推荐单个项目或项目列表，而是被推荐异构且类型多样对象的组合（称为包，例如，包括新闻、媒体和观看新闻的朋友）。与传统推荐不同，在包推荐中，包中的对象被明确显示给用户，用户会对显式展示的对象表现出极大的兴趣，反过来这些对象可能对用户行为产生重大影响，并显著改变传统的推荐模式。
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## 数据准备
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训练及测试数据集选自时间窗口为3天的微信公众号的用户日志，按7:1:2划分训练集、验证集、测试集
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在模型目录的data目录下为您准备了快速运行的示例数据，若需要使用全量数据可以参考下方效果复现部分。
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## 运行环境
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PaddlePaddle>=2.0
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python 2.7/3.5/3.6/3.7
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os : windows/linux/macos
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## 快速开始
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本文提供了样例数据可以供您快速体验，在任意目录下均可执行。在iprec模型目录的快速执行命令如下：
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```bash
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# 进入模型目录
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# cd models/rank/iprec # 在任意目录均可运行
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# 动态图训练
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python -u ../../../tools/trainer.py -m config.yaml # 全量数据运行config_bigdata.yaml
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# 动态图预测
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python -u ../../../tools/infer.py -m config.yaml
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```
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## 模型组网
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模型整体结构如下：
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<img align="center" src="../../../imgs/iprec.png">
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## 效果复现
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为了方便使用者能够快速的跑通每一个模型，我们在每个模型下都提供了样例数据。如果需要复现readme中的效果,请按如下步骤依次操作即可。
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在全量数据下模型的指标如下：
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| 模型 | auc | batch_size | epoch_num| Time of each epoch |
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|:------|:-------| :------ | :------| :------ |
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| iprec | 0.6934 | 256 | 1 | 3600s |
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1. 确认您当前所在目录为PaddleRec/models/rank/iprec
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2. 进入Paddlerec/datasets/iprec
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``` bash
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cd ../../../datasets/iprec
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sh run.sh
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```
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3. 切回模型目录,执行命令运行全量数据
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```bash
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cd - # 切回模型目录
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python -u ../../../tools/trainer.py -m config_bigdata.yaml
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python -u ../../../tools/infer.py -m config_bigdata.yaml
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```
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## 进阶使用
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## FAQ

‎doc/source/readme.md‎

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[autofis](https://paddlerec.readthedocs.io/en/latest/models/rank/autofis.html)
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[aitm](https://paddlerec.readthedocs.io/en/latest/models/rank/aitm.html)
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[dsin](https://paddlerec.readthedocs.io/en/latest/models/rank/dsin.html)
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[sign](https://paddlerec.readthedocs.io/en/latest/models/rank/sign.html)
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[sign](https://paddlerec.readthedocs.io/en/latest/models/rank/sign.html)
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[iprec](https://paddlerec.readthedocs.io/en/latest/models/rank/iprec.html)

‎models/rank/iprec/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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train_data_dir: "./data/sample_data/train"
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train_reader_path: "reader" # importlib format
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train_batch_size: 5
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model_save_path: "output_model_iprec"
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use_auc: True
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use_gpu: False
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epochs: 1
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print_interval: 1
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test_data_dir: "./data/sample_data/test"
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infer_reader_path: "reader" # importlib format
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infer_batch_size: 5
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infer_load_path: "output_model_iprec"
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infer_start_epoch: 0
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infer_end_epoch: 1
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hyper_parameters:
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optimizer:
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class: adam
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learning_rate: 0.0001
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num_users: 554237
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num_items: 344087
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num_bizs: 166465
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hidden_units: 64
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f_max_len: 20
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k: 4
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u_max_i: 99
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u_max_f: 220
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u_max_pack: 50
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pack_max_nei_b: 20
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pack_max_nei_f: 20
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dropout_rate: 0.4

‎models/rank/iprec/config_bigdata.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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train_data_dir: "../../../datasets/iprec/whole_data/train"
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train_reader_path: "reader" # importlib format
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train_batch_size: 256
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model_save_path: "output_model_iprec_all"
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use_auc: True
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use_gpu: True
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epochs: 5
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print_interval: 500
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test_data_dir: "../../../datasets/iprec/whole_data/test"
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infer_reader_path: "reader" # importlib format
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infer_batch_size: 256
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infer_load_path: "output_model_iprec_all"
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infer_start_epoch: 0
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infer_end_epoch: 5
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hyper_parameters:
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optimizer:
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class: adam
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learning_rate: 0.0001
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num_users: 554237
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num_items: 344087
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num_bizs: 166465
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hidden_units: 64
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f_max_len: 20
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k: 4
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u_max_i: 99
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u_max_f: 220
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u_max_pack: 50
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pack_max_nei_b: 20
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pack_max_nei_f: 20
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dropout_rate: 0.4

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