Add paper link, code link, and task category

This PR improves the dataset card by adding the `other` task category and relevant tags for recommendation systems. It also includes links to the associated research paper ("ManCAR: Manifold-Constrained Latent Reasoning with Adaptive Test-Time Computation for Sequential Recommendation") and the official GitHub repository for easier access to the implementation.

README.md

@@ -1,9 +1,16 @@
 ---
 license: mit
+task_categories:
+- other
+tags:
+- recommendation-system
+- sequential-recommendation
 ---
 
 # Amazon Reviews 2023 (7 Categories, Post-processed)
 
+[**Paper**](https://huggingface.co/papers/2602.20093) | [**Code**](https://github.com/FuCongResearchSquad/ManCAR)
+
 ## Overview
 This dataset is a curated and post-processed subset of Amazon Reviews 2023. 
 We select 7 product categories and apply a standard preprocessing pipeline widely used in sequential recommendation research.
@@ -29,13 +36,13 @@ The dataset is processed per category as follows:
 
 ## Directory Layout (per category)
 Each category has its own folder containing:
-* item.csv:  Primarily containing the remapped IDs for the items within that specific category.
+* `item.csv`: Primarily containing the remapped IDs for the items within that specific category.
 
-* train.csv: A file containing the interaction sequences used for training the model.
+* `train.csv`: A file containing the interaction sequences used for training the model.
 
-* valid.csv: A dedicated directory containing the validation sequences to tune hyperparameters and prevent overfitting.
+* `valid.csv`: A dedicated directory containing the validation sequences to tune hyperparameters and prevent overfitting.
 
-* test.csv: A directory containing the test sequences.
+* `test.csv`: A directory containing the test sequences.
 
 ## Licensing & Attribution
 This dataset is derived from Amazon Reviews 2023. Please refer to the original dataset page for licensing/usage terms and attribution requirements:
@@ -54,4 +61,5 @@ If you use this dataset, please cite:
   author={Kun Yang, Yuxuan Zhu, Yazhe Chen, Siyao Zheng, Bangyang Hong, Kangle Wu, Yabo Ni, Anxiang Zeng, Cong Fu, Hui Li},
   journal={arXiv preprint arXiv:2602.20093},
   year={2026}
-}
+}
+```