Implement DeepFM for CTR prediction #485
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…ification fix data reader error of text classification.
lcy-seso
reviewed
Nov 27, 2017
deep_fm/README.md
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| second_order = paddle.layer.factorization_machine(input=input, factor_size=factor_size) | ||
| fm = paddle.layer.addto(input=[first_order, second_order], | ||
| act=paddle.activation.Linear(), | ||
| ias_attr=False) |
deep_fm/README.md
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| ``` | ||
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| ## Evaluate | ||
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deep_fm/README.md
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| @@ -0,0 +1,90 @@ | |||
| # Deep Factorization Machines (DeepFM) for Click-Through Rate prediction | |||
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Deep Factorization Machine for Click-Through Rate prediction
deep_fm/README.md
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| A Factorization-Machine based Neural Network for CTR Prediction. | ||
| Proceedings of the Twenty-Sixth International Joint Conference on | ||
| Artificial Intelligence (IJCAI-17), 2017 | ||
| ``` |
deep_fm/README.md
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| Artificial Intelligence (IJCAI-17), 2017 | ||
| ``` | ||
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| The DeepFm combines factorization machines and deep neural networks to model |
deep_fm/infer.py
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| '--model_gz_path', | ||
| type=str, | ||
| required=True, | ||
| help="path of model parameters gz file") |
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参数的解释是一句完整的话，都变成大写字母开头，句号结尾吧。
Path 前面加上 the，下同。
| input=[first_order, second_order], | ||
| act=paddle.activation.Linear(), | ||
| bias_attr=False) | ||
| return out |
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line 7 ~ 19 可以作为一个helper 加入在Paddle repo下。这个等Paddle 下面的 PR merge 之后再加吧。
lcy-seso
approved these changes
Nov 27, 2017
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