Update README.md

README.md

@@ -14,4 +14,101 @@ datasets:
 metrics:
 - recall
 - ndcg
----
+---
+# 📖 Model Card: [REARM]
+
+
+**"[Refining Contrastive Learning and Homography Relations for Multi-Modal Recommendation]"**,  
+*Shouxing Ma, Yawen Zeng, Shiqing Wu, and Guandong Xu*  
+Published in *[ACM MM]*, 2025.  
+[[Paper Link](https://arxiv.org/abs/2508.13745)] [[Code Repository](https://github.com/MrShouxingMa/REARM)]
+---
+
+## ✨ Overview
+
+
+- We propose a novel multi-modal contrastive recommendation framework (REARM), which preserves recommendation-relevant modal-shared and valuable modal-unique information through meta-network and orthogonal constraint strategies, respectively.
+
+- We jointly incorporate co-occurrence and similarity graphs of users and items, allowing more effective capturing of the underlying structural patterns and semantic (interest) relationships, thereby enhancing recommendation performance.
+
+- Extensive experiments are conducted on three publicly available datasets to evaluate our proposed method. The experimental results show that our proposed framework outperforms several state-of-the-art recommendation baselines.
+---
+## 🧩 Environment Requirement
+The code has been tested running under Python 3.6. The required packages are as follows:
+
+* pytorch == 1.13.0
+* numpy == 1.24.4
+* scipy == 1.10.1
+
+## Data
+
+
+## Dataset
+
+We provide three processed datasets: Baby, Sports, and Clothing.
+
+| #Dataset   | #Interactions | #Users|#Items|Sparsity|
+|  ----  | ----  | ----  |----  |----  |
+|Baby|160,792|19,445|7,050|99.88%|
+|Sports|296,337|35,598|18,357|99.96%|
+|Clothing|278,677|39,387|23,033|99.97%|
+
+## 🚀 Example to Run the Codes
+
+The instructions for the commands are clearly stated in the codes.
+
+* Baby dataset
+```
+python main.py --dataset='baby'  --num_layer=4   --reg_weight=0.0005 --rank=3  --s_drop=0.4  --m_drop=0.6 --u_mm_image_weight=0.2  --i_mm_image_weight=0  --uu_co_weight=0.4 --ii_co_weight=0.2  --user_knn_k=40  --item_knn_k=10 --n_ii_layers=1 --n_uu_layers=1 --cl_tmp=0.6  --cl_loss_weight=5e-6 --diff_loss_weight=5e-5
+```
+
+* Sports dataset
+
+```
+python main.py --dataset='sports' --num_layer=5  --reg_weight=0.05 --rank=7  --s_drop=1  --m_drop=0.2 --u_mm_image_weight=0  --i_mm_image_weight=0.2  --uu_co_weight=0.9 --ii_co_weight=0.2  --user_knn_k=25  --item_knn_k=5 --n_ii_layers=2 --n_uu_layers=2 --cl_tmp=1.5  --cl_loss_weight=1e-3 --diff_loss_weight=5e-4
+```
+
+* Clothing dataset
+
+```
+python main.py --dataset='clothing' --num_layer=4  --reg_weight=0.00001 --rank=3  --s_drop=0.4  --m_drop=0.1 --u_mm_image_weight=0.1  --i_mm_image_weight=0.1  --uu_co_weight=0.7 --ii_co_weight=0.1  --user_knn_k=45  --item_knn_k=10 --n_ii_layers=1 --n_uu_layers=1 --cl_tmp=0.03  --cl_loss_weight=1e-6 --diff_loss_weight=1e-5
+```
+
+
+## REARM
+The released code consists of the following files.
+```
+--data
+    --baby
+    --clothing
+    --sports
+--utils
+    --configurator
+    --data_loader
+    --evaluator
+    --helper
+    --logger
+    --metrics
+    --parser              
+--main
+--model
+--trainer
+```
+
+## Citation
+If you want to use our codes and datasets in your research, please cite:
+
+``` 
+@inproceedings{REARM,
+  title     = {Refining Contrastive Learning and Homography Relations for Multi-Modal Recommendation,
+  author    = {Ma, Shouxing and 
+               Zeng, Yawen and 
+               Wu, Shiqing and 
+               Xu, Guandong},
+  booktitle = {Proceedings of the 33th ACM International Conference on Multimedia},
+  year      = {2025}
+}
+``` 
+
+
+