Merge pull request #24 from twndus/doc/wrap-up

Doc/wrap up
twndus · Sep 7, 2024 · cc18f18 · cc18f18
2 parents 63e91f8 + 5b19953
commit cc18f18
Showing 1 changed file with 87 additions and 10 deletions.
diff --git a/README.md b/README.md
@@ -12,37 +12,96 @@ This project focuses on matching benchmark performance in recommendation systems
 - **Collaborative Filtering**: Predicts user preferences based on user-item interactions.
   - **[Collaborative Denoising Auto-Encoders (2016)](https://alicezheng.org/papers/wsdm16-cdae.pdf)** applies Denoising Auto-Encoders (DAE) to top-N recommendation systems, generalizing various collaborative filtering (CF) models. Unlike AutoRec from 2015, CDAE incorporates a user node and uses corrupted input preferences.
 - **Matrix Factorization**: Reduces the dimensionality of the interaction matrix to uncover latent features.
+  - **[BPR: Bayesian Personalized Ranking from Implicit Feedback (2009)](https://arxiv.org/pdf/1205.2618)**: applies a pairwise ranking loss to leverage the performance of recommendation models, optimizing the training loss in a way that improves evaluation metrics like AUC.
 - **Deep Neural Networks**: Leverages deep learning to enhance prediction accuracy using complex feature interactions.
-- **Hybrid Models**: Integrates several models to capitalize on their individual strengths for superior performance.
+  - **[Deep & Cross Network for Ad Click Predictions (2017)](https://arxiv.org/pdf/1708.05123)**: incorporates explicit features crossing into a deep learning-based collaborative filtering model, efficiently capturing high-order interactions between signals.
+- **Sequential Models**: Predicts users' next item choice based on their past behaviors
+  - **[S3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization (2020)](https://arxiv.org/pdf/2008.07873)**: is a self-attention-based sequential recommendation model, pretrained by four distinct self-supervised objectives, leveraging Mutual Information Maximalzation.
+- **Graph-Convolution Models**: capture high-order interactions between users and items, enabling efficient batch-level computation
+  - **[Neural Graph Collaborative Filtering (2020)](https://arxiv.org/pdf/1905.08108)**: applies graph convolution to recommendation systems, incorporating high-order connectivity in an explicit manner compared to existing collaborative filtering methods.
 
 Our goal is to provide a robust analysis of these models and evaluate their performance comprehensively.
 
 ## Project Structure
 ```
 .
+├── README.md
+├── __init__.py
+├── configs
+│   ├── cdae_sweep_config.yaml
+│   ├── data_preprocess.yaml
+│   ├── mf_sweep_config.yaml
+│   ├── sweep_config.yaml
+│   └── train_config.yaml
+├── data
+│   ├── __init__.py
+│   ├── data_preprocess.py
+│   ├── datasets
+│   │   ├── __init__.py
+│   │   ├── cdae_data_pipeline.py
+│   │   ├── cdae_dataset.py
+│   │   ├── data_pipeline.py
+│   │   ├── dcn_data_pipeline.py
+│   │   ├── dcn_dataset.py
+│   │   ├── mf_data_pipeline.py
+│   │   ├── mf_dataset.py
+│   │   ├── ngcf_data_pipeline.py
+│   │   ├── ngcf_dataset.py
+│   │   ├── poprec_data_pipeline.py
+│   │   ├── poprec_dataset.py
+│   │   ├── s3rec_data_pipeline.py
+│   │   └── s3rec_dataset.py
+├── loss.py
+├── metric.py
+├── models
+│   ├── base_model.py
+│   ├── cdae.py
+│   ├── dcn.py
+│   ├── mf.py
+│   ├── ngcf.py
+│   ├── s3rec.py
+│   └── wdn.py
+├── poetry.lock
+├── pyproject.toml
+├── train.py
+├── trainers
+│   ├── __init__.py
+│   ├── base_trainer.py
+│   ├── cdae_trainer.py
+│   ├── dcn_trainer.py
+│   ├── mf_trainer.py
+│   ├── ngcf_trainer.py
+│   ├── poprec_trainer.py
+│   └── s3rec_trainer.py
+└── utils.py
 ```
 
 ## Development Environment
 To run this project, you will need:
-- **Python 3.8+**: Ensure Python version is up to date for compatibility.
+- **Python 3.11+**: Ensure Python version is up to date for compatibility.
 - **Jupyter Notebook**: For interactive data analysis and visualizations.
 - **Required Libraries**: pandas, numpy, scikit-learn, tensorflow/pytorch (depending on model choice).
 - **Operating System**: Compatible with Windows, macOS, and Linux.
 
 ## Technology Stack
-스택 나열
+![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)
+![PyTorch](https://img.shields.io/badge/PyTorch-%23EE4C2C.svg?style=for-the-badge&logo=PyTorch&logoColor=white)
+![Pandas](https://img.shields.io/badge/pandas-%23150458.svg?style=for-the-badge&logo=pandas&logoColor=white)
+![NumPy](https://img.shields.io/badge/numpy-%23013243.svg?style=for-the-badge&logo=numpy&logoColor=white)
+![Vim](https://img.shields.io/badge/VIM-%2311AB00.svg?style=for-the-badge&logo=vim&logoColor=white)
+![Google Cloud](https://img.shields.io/badge/GoogleCloud-%234285F4.svg?style=for-the-badge&logo=google-cloud&logoColor=white)
 
 ## Model Performance Comparison
 
 The following table shows the performance of different models used in the project. Each model was evaluated based on multiple metrics:
 
-| Model                   | MAP@10 | Precision@10 | Recall@10 | NDCG@10 |
-|-------------------------|----------|-----------|--------|----------|
-| CDAE                    | 82.5%    | 80.3%     | 84.1%  | 82.1%    |
-| DCN                     | 85.0%    | 83.7%     | 86.4%  | 85.0%    |
-| NGCF                    | 87.5%    | 85.8%     | 89.2%  | 87.4%    |
-| S3Rec                   | 90.2%    | 88.9%     | 91.5%  | 90.2%    |
-| Multi-armed bandit      | 90.2%    | 88.9%     | 91.5%  | 90.2%    |
+| Model                   | MAP@10 | Precision@10 | Recall@10 | NDCG@10 | HIT@10 | MRR |
+|-------------------------|----------|-----------|--------|----------|---------|-------|
+| CDAE                    | 0.02222    | 0.01538     | 0.0713  | 0.02198    | - | - |
+| DCN                     | 0.0004    | 0.0004     | 0.0016  | 0.0005    | - | - |
+| NGCF                    | 0.0002    | 0.0001     | 0.0006  | 0.0002    | - | - |
+| S3Rec                   | -    |   -   | -  | 0.1743  | 0.3134 | 0.1537 |
+| Multi-armed bandit      | -    | -     | -  | -   | - | - |
 
 These results were obtained from the Yelp 2018 dataset under controlled test conditions.
 
@@ -69,3 +128,21 @@ $ python train.py
 ```
 
 ## Contributors
+<table align="center">
+  <tr height="205px">
+    <td align="center" width="200px">
+      <a href="https://github.com/twndus"><img src="https://github.com/twndus.png" width="150px;" alt=""/></a>
+    </td>
+    <td align="center" width="200px">
+      <a href="https://github.com/GangBean"><img src="https://github.com/GangBean.png" width="150px;" alt=""/></a>
+    </td>
+  </tr>
+  <tr>
+    <td align="center" width="200px">
+      <a href="https://github.com/twndus">이주연</a>
+    </td>
+    <td align="center" width="200px">
+      <a href="https://github.com/GangBean">조성홍</a>
+    </td>
+  </tr>
+</table>