From 4b3592ebbc6e0ad2481dfde8fd4e44f5208351d6 Mon Sep 17 00:00:00 2001 From: CoprDistGit Date: Mon, 15 May 2023 06:18:56 +0000 Subject: automatic import of python-reclearn --- .gitignore | 1 + python-reclearn.spec | 816 +++++++++++++++++++++++++++++++++++++++++++++++++++ sources | 1 + 3 files changed, 818 insertions(+) create mode 100644 python-reclearn.spec create mode 100644 sources diff --git a/.gitignore b/.gitignore index e69de29..6b97b22 100644 --- a/.gitignore +++ b/.gitignore @@ -0,0 +1 @@ +/reclearn-1.1.0.tar.gz diff --git a/python-reclearn.spec b/python-reclearn.spec new file mode 100644 index 0000000..ecc1aa9 --- /dev/null +++ b/python-reclearn.spec @@ -0,0 +1,816 @@ +%global _empty_manifest_terminate_build 0 +Name: python-reclearn +Version: 1.1.0 +Release: 1 +Summary: A simple package about learning recommendation +License: MIT +URL: https://github.com/ZiyaoGeng/RecLearn +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/20/1b/7418016d4febdfef54b57359b747fed954ba8b775c4f974921a1c128fbe1/reclearn-1.1.0.tar.gz +BuildArch: noarch + + +%description +
+ +
+ +## RecLearn + +

+ + + + + + +

+ +[简体中文](https://github.com/ZiyaoGeng/Recommender-System-with-TF2.0/blob/reclearn/README_CN.md) | [English](https://github.com/ZiyaoGeng/Recommender-System-with-TF2.0/tree/reclearn) + +RecLearn (Recommender Learning) which summarizes the contents of the [master](https://github.com/ZiyaoGeng/RecLearn/tree/master) branch in `Recommender System with TF2.0 ` is a recommended learning framework based on Python and TensorFlow2.x for students and beginners. **Of course, if you are more comfortable with the master branch, you can clone the entire package, run some algorithms in example, and also update and modify the content of model and layer**. The implemented recommendation algorithms are classified according to two application stages in the industry: + +- matching recommendation stage (Top-k Recmmendation) +- ranking recommendeation stage (CTR predict model) + + + +## Update + +**04/23/2022**: update all matching model. + + + +## Installation + +### Package + +RecLearn is on PyPI, so you can use pip to install it. + +``` +pip install reclearn +``` + +dependent environment： + +- python3.8+ +- Tensorflow2.5-GPU+/Tensorflow2.5-CPU+ +- sklearn0.23+ + +### Local + +Clone Reclearn to local: + +```shell +git clone -b reclearn git@github.com:ZiyaoGeng/RecLearn.git +``` + + + +## Quick Start + +In [example](https://github.com/ZiyaoGeng/Recommender-System-with-TF2.0/tree/reclearn/example), we have given a demo of each of the recommended models. + +### Matching + +**1. Divide the dataset.** + +Set the path of the raw dataset: + +```python +file_path = 'data/ml-1m/ratings.dat' +``` + +Please divide the current dataset into training dataset, validation dataset and test dataset. If you use `movielens-1m`, `Amazon-Beauty`, `Amazon-Games` and `STEAM`, you can call method `data/datasets/*` of RecLearn directly: + +```python +train_path, val_path, test_path, meta_path = ml.split_seq_data(file_path=file_path) +``` + +`meta_path` indicates the path of the metafile, which stores the maximum number of user and item indexes. + +**2. Load the dataset.** + +Complete the loading of training dataset, validation dataset and test dataset, and generate several negative samples (random sampling) for each positive sample. The format of data is dictionary: + +```python +data = {'pos_item':, 'neg_item': , ['user': , 'click_seq': ,...]} +``` + +If you're building a sequential recommendation model, you need to introduce click sequences. Reclearn provides methods for loading the data for the above four datasets: + +```python +# general recommendation model +train_data = ml.load_data(train_path, neg_num, max_item_num) +# sequence recommendation model, and use the user feature. +train_data = ml.load_seq_data(train_path, "train", seq_len, neg_num, max_item_num, contain_user=True) +``` + +**3. Set hyper-parameters.** + +The model needs to specify the required hyperparameters. Now, we take `BPR` model as an example: + +```python +model_params = { + 'user_num': max_user_num + 1, + 'item_num': max_item_num + 1, + 'embed_dim': FLAGS.embed_dim, + 'use_l2norm': FLAGS.use_l2norm, + 'embed_reg': FLAGS.embed_reg + } +``` + +**4. Build and compile the model.** + +Select or build the model you need and compile it. Take 'BPR' as an example: + +```python +model = BPR(**model_params) +model.compile(optimizer=Adam(learning_rate=FLAGS.learning_rate)) +``` + +If you have problems with the structure of the model, you can call the summary method after compilation to print it out: + +```python +model.summary() +``` + +**5. Learn the model and predict test dataset.** + +```python +for epoch in range(1, epochs + 1): + t1 = time() + model.fit( + x=train_data, + epochs=1, + validation_data=val_data, + batch_size=batch_size + ) + t2 = time() + eval_dict = eval_pos_neg(model, test_data, ['hr', 'mrr', 'ndcg'], k, batch_size) + print('Iteration %d Fit [%.1f s], Evaluate [%.1f s]: HR = %.4f, MRR = %.4f, NDCG = %.4f' + % (epoch, t2 - t1, time() - t2, eval_dict['hr'], eval_dict['mrr'], eval_dict['ndcg'])) +``` + +### Ranking + +Waiting...... + + + +## Results + +The experimental environment designed by Reclearn is different from that of some papers, so there may be some deviation in the results. Please refer to [Experiement](./docs/experiment.md) for details. + +### Matching + + + + + + + + + + + + + + + + + + + + + + + +
Modelml-1mBeautySTEAM
HR@10MRR@10NDCG@10HR@10MRR@10NDCG@10HR@10MRR@10NDCG@10
BPR0.57680.23920.30160.37080.21080.24850.77280.42200.5054
NCF0.58340.22190.30600.54480.28310.34510.77680.42730.5103
DSSM0.54980.21480.2929------
YoutubeDNN0.67370.34140.4201------
GRU4Rec0.79690.46980.54830.52110.27240.33120.85010.54860.6209
Caser0.79160.44500.52800.54870.28840.35010.82750.50640.5832
SASRec0.81030.48120.56050.52300.27810.33550.86060.56690.6374
AttRec0.78730.45780.53630.49950.26950.3229---
FISSA0.81060.49530.57130.54310.28510.34620.86350.56820.6391
+ + + +### Ranking + + + + + + + + + + + + + + + + + + + + + + + + +
Model500w(Criteo)Criteo
Log LossAUCLog LossAUC
FM0.47650.77830.47620.7875
FFM----
WDL0.46840.78220.46920.7930
Deep Crossing0.46700.78260.46930.7935
PNN-0.7847--
DCN-0.78230.46910.7929
NFM0.47730.77620.47230.7889
AFM0.48190.78080.46920.7871
DeepFM-0.78280.46500.8007
xDeepFM0.46900.78390.46960.7919
+ + +## Model List + +### 1. Matching Stage + +| Paper\|Model | Published | Author | +| :----------------------------------------------------------: | :----------: | :------------: | +| BPR: Bayesian Personalized Ranking from Implicit Feedback\|**MF-BPR** | UAI, 2009 | Steffen Rendle | +| Neural network-based Collaborative Filtering\|**NCF** | WWW, 2017 | Xiangnan He | +| Learning Deep Structured Semantic Models for Web Search using Clickthrough Data\|**DSSM** | CIKM, 2013 | Po-Sen Huang | +| Deep Neural Networks for YouTube Recommendations\| **YoutubeDNN** | RecSys, 2016 | Paul Covington | +| Session-based Recommendations with Recurrent Neural Networks\|**GUR4Rec** | ICLR, 2016 | Balázs Hidasi | +| Self-Attentive Sequential Recommendation\|**SASRec** | ICDM, 2018 | UCSD | +| Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding\|**Caser** | WSDM, 2018 | Jiaxi Tang | +| Next Item Recommendation with Self-Attentive Metric Learning\|**AttRec** | AAAAI, 2019 | Shuai Zhang | +| FISSA: Fusing Item Similarity Models with Self-Attention Networks for Sequential Recommendation\|**FISSA** | RecSys, 2020 | Jing Lin | + +### 2. Ranking Stage + +| Paper｜Model | Published | Author | +| :----------------------------------------------------------: | :----------: | :----------------------------------------------------------: | +| Factorization Machines\|**FM** | ICDM, 2010 | Steffen Rendle | +| Field-aware Factorization Machines for CTR Prediction｜**FFM** | RecSys, 2016 | Criteo Research | +| Wide & Deep Learning for Recommender Systems｜**WDL** | DLRS, 2016 | Google Inc. | +| Deep Crossing: Web-Scale Modeling without Manually Crafted Combinatorial Features\|**Deep Crossing** | KDD, 2016 | Microsoft Research | +| Product-based Neural Networks for User Response Prediction\|**PNN** | ICDM, 2016 | Shanghai Jiao Tong University | +| Deep & Cross Network for Ad Click Predictions｜**DCN** | ADKDD, 2017 | Stanford University｜Google Inc. | +| Neural Factorization Machines for Sparse Predictive Analytics\|**NFM** | SIGIR, 2017 | Xiangnan He | +| Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks\|**AFM** | IJCAI, 2017 | Zhejiang University\|National University of Singapore | +| DeepFM: A Factorization-Machine based Neural Network for CTR Prediction\|**DeepFM** | IJCAI, 2017 | Harbin Institute of Technology\|Noah’s Ark Research Lab, Huawei | +| xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems\|**xDeepFM** | KDD, 2018 | University of Science and Technology of China | +| Deep Interest Network for Click-Through Rate Prediction\|**DIN** | KDD, 2018 | Alibaba Group | + +## Discussion + +1. If you have any suggestions or questions about the project, you can leave a comment on `Issue`. +2. wechat： + +
+ + + + + +%package -n python3-reclearn +Summary: A simple package about learning recommendation +Provides: python-reclearn +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-reclearn +
+ +
+ +## RecLearn + +

+ + + + + + +

+ +[简体中文](https://github.com/ZiyaoGeng/Recommender-System-with-TF2.0/blob/reclearn/README_CN.md) | [English](https://github.com/ZiyaoGeng/Recommender-System-with-TF2.0/tree/reclearn) + +RecLearn (Recommender Learning) which summarizes the contents of the [master](https://github.com/ZiyaoGeng/RecLearn/tree/master) branch in `Recommender System with TF2.0 ` is a recommended learning framework based on Python and TensorFlow2.x for students and beginners. **Of course, if you are more comfortable with the master branch, you can clone the entire package, run some algorithms in example, and also update and modify the content of model and layer**. The implemented recommendation algorithms are classified according to two application stages in the industry: + +- matching recommendation stage (Top-k Recmmendation) +- ranking recommendeation stage (CTR predict model) + + + +## Update + +**04/23/2022**: update all matching model. + + + +## Installation + +### Package + +RecLearn is on PyPI, so you can use pip to install it. + +``` +pip install reclearn +``` + +dependent environment： + +- python3.8+ +- Tensorflow2.5-GPU+/Tensorflow2.5-CPU+ +- sklearn0.23+ + +### Local + +Clone Reclearn to local: + +```shell +git clone -b reclearn git@github.com:ZiyaoGeng/RecLearn.git +``` + + + +## Quick Start + +In [example](https://github.com/ZiyaoGeng/Recommender-System-with-TF2.0/tree/reclearn/example), we have given a demo of each of the recommended models. + +### Matching + +**1. Divide the dataset.** + +Set the path of the raw dataset: + +```python +file_path = 'data/ml-1m/ratings.dat' +``` + +Please divide the current dataset into training dataset, validation dataset and test dataset. If you use `movielens-1m`, `Amazon-Beauty`, `Amazon-Games` and `STEAM`, you can call method `data/datasets/*` of RecLearn directly: + +```python +train_path, val_path, test_path, meta_path = ml.split_seq_data(file_path=file_path) +``` + +`meta_path` indicates the path of the metafile, which stores the maximum number of user and item indexes. + +**2. Load the dataset.** + +Complete the loading of training dataset, validation dataset and test dataset, and generate several negative samples (random sampling) for each positive sample. The format of data is dictionary: + +```python +data = {'pos_item':, 'neg_item': , ['user': , 'click_seq': ,...]} +``` + +If you're building a sequential recommendation model, you need to introduce click sequences. Reclearn provides methods for loading the data for the above four datasets: + +```python +# general recommendation model +train_data = ml.load_data(train_path, neg_num, max_item_num) +# sequence recommendation model, and use the user feature. +train_data = ml.load_seq_data(train_path, "train", seq_len, neg_num, max_item_num, contain_user=True) +``` + +**3. Set hyper-parameters.** + +The model needs to specify the required hyperparameters. Now, we take `BPR` model as an example: + +```python +model_params = { + 'user_num': max_user_num + 1, + 'item_num': max_item_num + 1, + 'embed_dim': FLAGS.embed_dim, + 'use_l2norm': FLAGS.use_l2norm, + 'embed_reg': FLAGS.embed_reg + } +``` + +**4. Build and compile the model.** + +Select or build the model you need and compile it. Take 'BPR' as an example: + +```python +model = BPR(**model_params) +model.compile(optimizer=Adam(learning_rate=FLAGS.learning_rate)) +``` + +If you have problems with the structure of the model, you can call the summary method after compilation to print it out: + +```python +model.summary() +``` + +**5. Learn the model and predict test dataset.** + +```python +for epoch in range(1, epochs + 1): + t1 = time() + model.fit( + x=train_data, + epochs=1, + validation_data=val_data, + batch_size=batch_size + ) + t2 = time() + eval_dict = eval_pos_neg(model, test_data, ['hr', 'mrr', 'ndcg'], k, batch_size) + print('Iteration %d Fit [%.1f s], Evaluate [%.1f s]: HR = %.4f, MRR = %.4f, NDCG = %.4f' + % (epoch, t2 - t1, time() - t2, eval_dict['hr'], eval_dict['mrr'], eval_dict['ndcg'])) +``` + +### Ranking + +Waiting...... + + + +## Results + +The experimental environment designed by Reclearn is different from that of some papers, so there may be some deviation in the results. Please refer to [Experiement](./docs/experiment.md) for details. + +### Matching + + + + + + + + + + + + + + + + + + + + + + + +
Modelml-1mBeautySTEAM
HR@10MRR@10NDCG@10HR@10MRR@10NDCG@10HR@10MRR@10NDCG@10
BPR0.57680.23920.30160.37080.21080.24850.77280.42200.5054
NCF0.58340.22190.30600.54480.28310.34510.77680.42730.5103
DSSM0.54980.21480.2929------
YoutubeDNN0.67370.34140.4201------
GRU4Rec0.79690.46980.54830.52110.27240.33120.85010.54860.6209
Caser0.79160.44500.52800.54870.28840.35010.82750.50640.5832
SASRec0.81030.48120.56050.52300.27810.33550.86060.56690.6374
AttRec0.78730.45780.53630.49950.26950.3229---
FISSA0.81060.49530.57130.54310.28510.34620.86350.56820.6391
+ + + +### Ranking + + + + + + + + + + + + + + + + + + + + + + + + +
Model500w(Criteo)Criteo
Log LossAUCLog LossAUC
FM0.47650.77830.47620.7875
FFM----
WDL0.46840.78220.46920.7930
Deep Crossing0.46700.78260.46930.7935
PNN-0.7847--
DCN-0.78230.46910.7929
NFM0.47730.77620.47230.7889
AFM0.48190.78080.46920.7871
DeepFM-0.78280.46500.8007
xDeepFM0.46900.78390.46960.7919
+ + +## Model List + +### 1. Matching Stage + +| Paper\|Model | Published | Author | +| :----------------------------------------------------------: | :----------: | :------------: | +| BPR: Bayesian Personalized Ranking from Implicit Feedback\|**MF-BPR** | UAI, 2009 | Steffen Rendle | +| Neural network-based Collaborative Filtering\|**NCF** | WWW, 2017 | Xiangnan He | +| Learning Deep Structured Semantic Models for Web Search using Clickthrough Data\|**DSSM** | CIKM, 2013 | Po-Sen Huang | +| Deep Neural Networks for YouTube Recommendations\| **YoutubeDNN** | RecSys, 2016 | Paul Covington | +| Session-based Recommendations with Recurrent Neural Networks\|**GUR4Rec** | ICLR, 2016 | Balázs Hidasi | +| Self-Attentive Sequential Recommendation\|**SASRec** | ICDM, 2018 | UCSD | +| Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding\|**Caser** | WSDM, 2018 | Jiaxi Tang | +| Next Item Recommendation with Self-Attentive Metric Learning\|**AttRec** | AAAAI, 2019 | Shuai Zhang | +| FISSA: Fusing Item Similarity Models with Self-Attention Networks for Sequential Recommendation\|**FISSA** | RecSys, 2020 | Jing Lin | + +### 2. Ranking Stage + +| Paper｜Model | Published | Author | +| :----------------------------------------------------------: | :----------: | :----------------------------------------------------------: | +| Factorization Machines\|**FM** | ICDM, 2010 | Steffen Rendle | +| Field-aware Factorization Machines for CTR Prediction｜**FFM** | RecSys, 2016 | Criteo Research | +| Wide & Deep Learning for Recommender Systems｜**WDL** | DLRS, 2016 | Google Inc. | +| Deep Crossing: Web-Scale Modeling without Manually Crafted Combinatorial Features\|**Deep Crossing** | KDD, 2016 | Microsoft Research | +| Product-based Neural Networks for User Response Prediction\|**PNN** | ICDM, 2016 | Shanghai Jiao Tong University | +| Deep & Cross Network for Ad Click Predictions｜**DCN** | ADKDD, 2017 | Stanford University｜Google Inc. | +| Neural Factorization Machines for Sparse Predictive Analytics\|**NFM** | SIGIR, 2017 | Xiangnan He | +| Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks\|**AFM** | IJCAI, 2017 | Zhejiang University\|National University of Singapore | +| DeepFM: A Factorization-Machine based Neural Network for CTR Prediction\|**DeepFM** | IJCAI, 2017 | Harbin Institute of Technology\|Noah’s Ark Research Lab, Huawei | +| xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems\|**xDeepFM** | KDD, 2018 | University of Science and Technology of China | +| Deep Interest Network for Click-Through Rate Prediction\|**DIN** | KDD, 2018 | Alibaba Group | + +## Discussion + +1. If you have any suggestions or questions about the project, you can leave a comment on `Issue`. +2. wechat： + +
+ + + + + +%package help +Summary: Development documents and examples for reclearn +Provides: python3-reclearn-doc +%description help +
+ +
+ +## RecLearn + +

+ + + + + + +

+ +[简体中文](https://github.com/ZiyaoGeng/Recommender-System-with-TF2.0/blob/reclearn/README_CN.md) | [English](https://github.com/ZiyaoGeng/Recommender-System-with-TF2.0/tree/reclearn) + +RecLearn (Recommender Learning) which summarizes the contents of the [master](https://github.com/ZiyaoGeng/RecLearn/tree/master) branch in `Recommender System with TF2.0 ` is a recommended learning framework based on Python and TensorFlow2.x for students and beginners. **Of course, if you are more comfortable with the master branch, you can clone the entire package, run some algorithms in example, and also update and modify the content of model and layer**. The implemented recommendation algorithms are classified according to two application stages in the industry: + +- matching recommendation stage (Top-k Recmmendation) +- ranking recommendeation stage (CTR predict model) + + + +## Update + +**04/23/2022**: update all matching model. + + + +## Installation + +### Package + +RecLearn is on PyPI, so you can use pip to install it. + +``` +pip install reclearn +``` + +dependent environment： + +- python3.8+ +- Tensorflow2.5-GPU+/Tensorflow2.5-CPU+ +- sklearn0.23+ + +### Local + +Clone Reclearn to local: + +```shell +git clone -b reclearn git@github.com:ZiyaoGeng/RecLearn.git +``` + + + +## Quick Start + +In [example](https://github.com/ZiyaoGeng/Recommender-System-with-TF2.0/tree/reclearn/example), we have given a demo of each of the recommended models. + +### Matching + +**1. Divide the dataset.** + +Set the path of the raw dataset: + +```python +file_path = 'data/ml-1m/ratings.dat' +``` + +Please divide the current dataset into training dataset, validation dataset and test dataset. If you use `movielens-1m`, `Amazon-Beauty`, `Amazon-Games` and `STEAM`, you can call method `data/datasets/*` of RecLearn directly: + +```python +train_path, val_path, test_path, meta_path = ml.split_seq_data(file_path=file_path) +``` + +`meta_path` indicates the path of the metafile, which stores the maximum number of user and item indexes. + +**2. Load the dataset.** + +Complete the loading of training dataset, validation dataset and test dataset, and generate several negative samples (random sampling) for each positive sample. The format of data is dictionary: + +```python +data = {'pos_item':, 'neg_item': , ['user': , 'click_seq': ,...]} +``` + +If you're building a sequential recommendation model, you need to introduce click sequences. Reclearn provides methods for loading the data for the above four datasets: + +```python +# general recommendation model +train_data = ml.load_data(train_path, neg_num, max_item_num) +# sequence recommendation model, and use the user feature. +train_data = ml.load_seq_data(train_path, "train", seq_len, neg_num, max_item_num, contain_user=True) +``` + +**3. Set hyper-parameters.** + +The model needs to specify the required hyperparameters. Now, we take `BPR` model as an example: + +```python +model_params = { + 'user_num': max_user_num + 1, + 'item_num': max_item_num + 1, + 'embed_dim': FLAGS.embed_dim, + 'use_l2norm': FLAGS.use_l2norm, + 'embed_reg': FLAGS.embed_reg + } +``` + +**4. Build and compile the model.** + +Select or build the model you need and compile it. Take 'BPR' as an example: + +```python +model = BPR(**model_params) +model.compile(optimizer=Adam(learning_rate=FLAGS.learning_rate)) +``` + +If you have problems with the structure of the model, you can call the summary method after compilation to print it out: + +```python +model.summary() +``` + +**5. Learn the model and predict test dataset.** + +```python +for epoch in range(1, epochs + 1): + t1 = time() + model.fit( + x=train_data, + epochs=1, + validation_data=val_data, + batch_size=batch_size + ) + t2 = time() + eval_dict = eval_pos_neg(model, test_data, ['hr', 'mrr', 'ndcg'], k, batch_size) + print('Iteration %d Fit [%.1f s], Evaluate [%.1f s]: HR = %.4f, MRR = %.4f, NDCG = %.4f' + % (epoch, t2 - t1, time() - t2, eval_dict['hr'], eval_dict['mrr'], eval_dict['ndcg'])) +``` + +### Ranking + +Waiting...... + + + +## Results + +The experimental environment designed by Reclearn is different from that of some papers, so there may be some deviation in the results. Please refer to [Experiement](./docs/experiment.md) for details. + +### Matching + + + + + + + + + + + + + + + + + + + + + + + +
Modelml-1mBeautySTEAM
HR@10MRR@10NDCG@10HR@10MRR@10NDCG@10HR@10MRR@10NDCG@10
BPR0.57680.23920.30160.37080.21080.24850.77280.42200.5054
NCF0.58340.22190.30600.54480.28310.34510.77680.42730.5103
DSSM0.54980.21480.2929------
YoutubeDNN0.67370.34140.4201------
GRU4Rec0.79690.46980.54830.52110.27240.33120.85010.54860.6209
Caser0.79160.44500.52800.54870.28840.35010.82750.50640.5832
SASRec0.81030.48120.56050.52300.27810.33550.86060.56690.6374
AttRec0.78730.45780.53630.49950.26950.3229---
FISSA0.81060.49530.57130.54310.28510.34620.86350.56820.6391
+ + + +### Ranking + + + + + + + + + + + + + + + + + + + + + + + + +
Model500w(Criteo)Criteo
Log LossAUCLog LossAUC
FM0.47650.77830.47620.7875
FFM----
WDL0.46840.78220.46920.7930
Deep Crossing0.46700.78260.46930.7935
PNN-0.7847--
DCN-0.78230.46910.7929
NFM0.47730.77620.47230.7889
AFM0.48190.78080.46920.7871
DeepFM-0.78280.46500.8007
xDeepFM0.46900.78390.46960.7919
+ + +## Model List + +### 1. Matching Stage + +| Paper\|Model | Published | Author | +| :----------------------------------------------------------: | :----------: | :------------: | +| BPR: Bayesian Personalized Ranking from Implicit Feedback\|**MF-BPR** | UAI, 2009 | Steffen Rendle | +| Neural network-based Collaborative Filtering\|**NCF** | WWW, 2017 | Xiangnan He | +| Learning Deep Structured Semantic Models for Web Search using Clickthrough Data\|**DSSM** | CIKM, 2013 | Po-Sen Huang | +| Deep Neural Networks for YouTube Recommendations\| **YoutubeDNN** | RecSys, 2016 | Paul Covington | +| Session-based Recommendations with Recurrent Neural Networks\|**GUR4Rec** | ICLR, 2016 | Balázs Hidasi | +| Self-Attentive Sequential Recommendation\|**SASRec** | ICDM, 2018 | UCSD | +| Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding\|**Caser** | WSDM, 2018 | Jiaxi Tang | +| Next Item Recommendation with Self-Attentive Metric Learning\|**AttRec** | AAAAI, 2019 | Shuai Zhang | +| FISSA: Fusing Item Similarity Models with Self-Attention Networks for Sequential Recommendation\|**FISSA** | RecSys, 2020 | Jing Lin | + +### 2. Ranking Stage + +| Paper｜Model | Published | Author | +| :----------------------------------------------------------: | :----------: | :----------------------------------------------------------: | +| Factorization Machines\|**FM** | ICDM, 2010 | Steffen Rendle | +| Field-aware Factorization Machines for CTR Prediction｜**FFM** | RecSys, 2016 | Criteo Research | +| Wide & Deep Learning for Recommender Systems｜**WDL** | DLRS, 2016 | Google Inc. | +| Deep Crossing: Web-Scale Modeling without Manually Crafted Combinatorial Features\|**Deep Crossing** | KDD, 2016 | Microsoft Research | +| Product-based Neural Networks for User Response Prediction\|**PNN** | ICDM, 2016 | Shanghai Jiao Tong University | +| Deep & Cross Network for Ad Click Predictions｜**DCN** | ADKDD, 2017 | Stanford University｜Google Inc. | +| Neural Factorization Machines for Sparse Predictive Analytics\|**NFM** | SIGIR, 2017 | Xiangnan He | +| Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks\|**AFM** | IJCAI, 2017 | Zhejiang University\|National University of Singapore | +| DeepFM: A Factorization-Machine based Neural Network for CTR Prediction\|**DeepFM** | IJCAI, 2017 | Harbin Institute of Technology\|Noah’s Ark Research Lab, Huawei | +| xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems\|**xDeepFM** | KDD, 2018 | University of Science and Technology of China | +| Deep Interest Network for Click-Through Rate Prediction\|**DIN** | KDD, 2018 | Alibaba Group | + +## Discussion + +1. If you have any suggestions or questions about the project, you can leave a comment on `Issue`. +2. wechat： + +
+ + + + + +%prep +%autosetup -n reclearn-1.1.0 + +%build +%py3_build + +%install +%py3_install +install -d -m755 %{buildroot}/%{_pkgdocdir} +if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi +if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi +if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi +if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi +pushd %{buildroot} +if [ -d usr/lib ]; then + find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/lib64 ]; then + find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/bin ]; then + find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/sbin ]; then + find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst +fi +touch doclist.lst +if [ -d usr/share/man ]; then + find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst +fi +popd +mv %{buildroot}/filelist.lst . +mv %{buildroot}/doclist.lst . + +%files -n python3-reclearn -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Mon May 15 2023 Python_Bot - 1.1.0-1 +- Package Spec generated diff --git a/sources b/sources new file mode 100644 index 0000000..80a428a --- /dev/null +++ b/sources @@ -0,0 +1 @@ +e657704583405b15aa070ab76eaddf95 reclearn-1.1.0.tar.gz -- cgit v1.2.3