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Add GDCN implementation from CIKM2023 (#73)
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* GDCN implementation

Implementation of Towards Deeper, Lighter and Interpretable Cross Network for CTR Prediction
https://dl.acm.org/doi/pdf/10.1145/3583780.3615089

* Update README.md

* Update LICENSE

* Update README.md

* Update GDCN.py
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sdilbaz authored Nov 2, 2023
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17 changes: 16 additions & 1 deletion LICENSE
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For layers.ScaledDotProductAttention:
Copyright (C) 2018 pengshuang@Github

-----------------------------------------------------------------------

For models.DCNv2.CrossNetMix:
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
-----------------------------------------------------------------------

For model_zoo.GDCN:
Copyright (C) 2023 sdilbaz@github

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
27 changes: 14 additions & 13 deletions README.md
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Expand Up @@ -72,23 +72,24 @@ Click-through rate (CTR) prediction is a critical task for many industrial appli
| 35 | KDD'21 | [AOANet](./model_zoo/AOANet) | [Architecture and Operation Adaptive Network for Online Recommendations](https://dl.acm.org/doi/10.1145/3447548.3467133) :triangular_flag_on_post:**Didi Chuxing** | [:arrow_upper_right:](https://github.com/openbenchmark/BARS/tree/main/ctr_prediction/benchmarks/AOANet) | `torch` |
| 36 | AAAI'23 | [FinalMLP](./model_zoo/FinalMLP) | [FinalMLP: An Enhanced Two-Stream MLP Model for CTR Prediction](https://arxiv.org/abs/2304.00902) :triangular_flag_on_post:**Huawei** | [:arrow_upper_right:](https://github.com/openbenchmark/BARS/tree/main/ctr_prediction/benchmarks/FinalMLP) | `torch` |
| 37 | SIGIR'23 | [FINAL](./model_zoo/FINAL) | FINAL: Factorized Interaction Layer for CTR Prediction :triangular_flag_on_post:**Huawei** | [:arrow_upper_right:](https://github.com/openbenchmark/BARS/tree/main/ctr_prediction/benchmarks/FINAL) | `torch` |
| 38 | CIKM'23 | [GDCN](./model_zoo/GDCN) | [Towards Deeper, Lighter and Interpretable Cross Network for CTR Prediction](https://dl.acm.org/doi/pdf/10.1145/3583780.3615089) :triangular_flag_on_post:**Microsoft** | [:arrow_upper_right:](https://github.com/openbenchmark/BARS/tree/main/ctr_prediction/benchmarks/FinalMLP) | `torch` |
|<tr><th colspan=6 align="center">:open_file_folder: **Behavior Sequence Modeling**</th></tr>|
| 38 | KDD'18 | [DIN](./model_zoo/DIN) | [Deep Interest Network for Click-Through Rate Prediction](https://www.kdd.org/kdd2018/accepted-papers/view/deep-interest-network-for-click-through-rate-prediction) :triangular_flag_on_post:**Alibaba** | [:arrow_upper_right:](https://github.com/openbenchmark/BARS/tree/main/ctr_prediction/benchmarks/DIN) | `torch` |
| 39 | AAAI'19 | [DIEN](./model_zoo/DIEN) | [Deep Interest Evolution Network for Click-Through Rate Prediction](https://arxiv.org/abs/1809.03672) :triangular_flag_on_post:**Alibaba** | [:arrow_upper_right:](https://github.com/openbenchmark/BARS/tree/main/ctr_prediction/benchmarks/DIEN) | `torch` |
| 40 | DLP-KDD'19 | [BST](./model_zoo/BST) | [Behavior Sequence Transformer for E-commerce Recommendation in Alibaba](https://arxiv.org/abs/1905.06874) :triangular_flag_on_post:**Alibaba** | [:arrow_upper_right:](https://github.com/openbenchmark/BARS/tree/main/ctr_prediction/benchmarks/BST) | `torch` |
| 41 | CIKM'20 | [DMIN](./model_zoo/DMIN) | [Deep Multi-Interest Network for Click-through Rate Prediction](https://dl.acm.org/doi/10.1145/3340531.3412092) :triangular_flag_on_post:**Alibaba** | | `torch` |
| 42 | AAAI'20 | [DMR](./model_zoo/DMR) | [Deep Match to Rank Model for Personalized Click-Through Rate Prediction](https://ojs.aaai.org/index.php/AAAI/article/view/5346) :triangular_flag_on_post:**Alibaba** | | `torch` |
| 43 | Arxiv'21 | [ETA](./model_zoo/ETA) | [End-to-End User Behavior Retrieval in Click-Through RatePrediction Model](https://arxiv.org/abs/2108.04468) :triangular_flag_on_post:**Alibaba** | | `torch` |
| 44 | CIKM'22 | [SDIM](./model_zoo/SDIM) | [Sampling Is All You Need on Modeling Long-Term User Behaviors for CTR Prediction](https://arxiv.org/abs/2205.10249) :triangular_flag_on_post:**Meituan** | | `torch` |
| 39 | KDD'18 | [DIN](./model_zoo/DIN) | [Deep Interest Network for Click-Through Rate Prediction](https://www.kdd.org/kdd2018/accepted-papers/view/deep-interest-network-for-click-through-rate-prediction) :triangular_flag_on_post:**Alibaba** | [:arrow_upper_right:](https://github.com/openbenchmark/BARS/tree/main/ctr_prediction/benchmarks/DIN) | `torch` |
| 40 | AAAI'19 | [DIEN](./model_zoo/DIEN) | [Deep Interest Evolution Network for Click-Through Rate Prediction](https://arxiv.org/abs/1809.03672) :triangular_flag_on_post:**Alibaba** | [:arrow_upper_right:](https://github.com/openbenchmark/BARS/tree/main/ctr_prediction/benchmarks/DIEN) | `torch` |
| 41 | DLP-KDD'19 | [BST](./model_zoo/BST) | [Behavior Sequence Transformer for E-commerce Recommendation in Alibaba](https://arxiv.org/abs/1905.06874) :triangular_flag_on_post:**Alibaba** | [:arrow_upper_right:](https://github.com/openbenchmark/BARS/tree/main/ctr_prediction/benchmarks/BST) | `torch` |
| 42 | CIKM'20 | [DMIN](./model_zoo/DMIN) | [Deep Multi-Interest Network for Click-through Rate Prediction](https://dl.acm.org/doi/10.1145/3340531.3412092) :triangular_flag_on_post:**Alibaba** | | `torch` |
| 43 | AAAI'20 | [DMR](./model_zoo/DMR) | [Deep Match to Rank Model for Personalized Click-Through Rate Prediction](https://ojs.aaai.org/index.php/AAAI/article/view/5346) :triangular_flag_on_post:**Alibaba** | | `torch` |
| 44 | Arxiv'21 | [ETA](./model_zoo/ETA) | [End-to-End User Behavior Retrieval in Click-Through RatePrediction Model](https://arxiv.org/abs/2108.04468) :triangular_flag_on_post:**Alibaba** | | `torch` |
| 45 | CIKM'22 | [SDIM](./model_zoo/SDIM) | [Sampling Is All You Need on Modeling Long-Term User Behaviors for CTR Prediction](https://arxiv.org/abs/2205.10249) :triangular_flag_on_post:**Meituan** | | `torch` |
|<tr><th colspan=6 align="center">:open_file_folder: **Dynamic Weight Network**</th></tr>|
| 45 | NeurIPS'22 | [APG](./model_zoo/APG) | [APG: Adaptive Parameter Generation Network for Click-Through Rate Prediction](https://arxiv.org/abs/2203.16218) :triangular_flag_on_post:**Alibaba** | | `torch` |
| 46 | Arxiv'23 | [PPNet](./model_zoo/PEPNet) | [PEPNet: Parameter and Embedding Personalized Network for Infusing with Personalized Prior Information](https://arxiv.org/abs/2302.01115) :triangular_flag_on_post:**KuaiShou** | | `torch` |
| 46 | NeurIPS'22 | [APG](./model_zoo/APG) | [APG: Adaptive Parameter Generation Network for Click-Through Rate Prediction](https://arxiv.org/abs/2203.16218) :triangular_flag_on_post:**Alibaba** | | `torch` |
| 47 | Arxiv'23 | [PPNet](./model_zoo/PEPNet) | [PEPNet: Parameter and Embedding Personalized Network for Infusing with Personalized Prior Information](https://arxiv.org/abs/2302.01115) :triangular_flag_on_post:**KuaiShou** | | `torch` |
|<tr><th colspan=6 align="center">:open_file_folder: **Multi-Task Modeling**</th></tr>|
| 47 | MachineLearn'97 | [SharedBottom](./model_zoo/multitask/SharedBottom) | [Multitask Learning](https://link.springer.com/article/10.1023/A:1007379606734) | | `torch` |
| 48 | KDD'18 | [MMoE](./model_zoo/multitask/MMOE) | [Modeling Task Relationships in Multi-task Learning with Multi-Gate Mixture-of-Experts](https://dl.acm.org/doi/pdf/10.1145/3219819.3220007) :triangular_flag_on_post:**Google** | | `torch` |
| 49 | KDD'18 | [PLE](./model_zoo/multitask/PLE) | [Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations](https://dl.acm.org/doi/10.1145/3383313.3412236) :triangular_flag_on_post:**Tencent** | | `torch` |
| 48 | MachineLearn'97 | [SharedBottom](./model_zoo/multitask/SharedBottom) | [Multitask Learning](https://link.springer.com/article/10.1023/A:1007379606734) | | `torch` |
| 49 | KDD'18 | [MMoE](./model_zoo/multitask/MMOE) | [Modeling Task Relationships in Multi-task Learning with Multi-Gate Mixture-of-Experts](https://dl.acm.org/doi/pdf/10.1145/3219819.3220007) :triangular_flag_on_post:**Google** | | `torch` |
| 50 | KDD'18 | [PLE](./model_zoo/multitask/PLE) | [Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations](https://dl.acm.org/doi/10.1145/3383313.3412236) :triangular_flag_on_post:**Tencent** | | `torch` |
|<tr><th colspan=6 align="center">:open_file_folder: **Multi-Domain Modeling**</th></tr>|
| 50 | Arxiv'23 | PEPNet | [PEPNet: Parameter and Embedding Personalized Network for Infusing with Personalized Prior Information](https://arxiv.org/abs/2302.01115) :triangular_flag_on_post:**KuaiShou** | | `torch` |
| 51 | Arxiv'23 | PEPNet | [PEPNet: Parameter and Embedding Personalized Network for Infusing with Personalized Prior Information](https://arxiv.org/abs/2302.01115) :triangular_flag_on_post:**KuaiShou** | | `torch` |


+ :point_right: See [reusable dataset splits for CTR prediction](https://openbenchmark.github.io/BARS/datasets/README.html).
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