Add link to code and paper (#3)

- Add link to code and paper (a9addb4e0dd6afc827155f5b1d0ae3c0706db4f1)


Co-authored-by: Niels Rogge <[email protected]>

README.md

@@ -1,4 +1,7 @@
 ---
+license: cc-by-nc-4.0
+task_categories:
+- text-generation
 dataset_info:
 - config_name: Books
   features:
@@ -145,19 +148,18 @@ configs:
     path: Movies_and_TV/val-*
   - split: test
     path: Movies_and_TV/test-*
-license: cc-by-nc-4.0
-task_categories:
-- text-generation
 ---
 
 # Difference-aware Personalized Learning (DPL) Dataset
 
 This dataset is used in the paper:
 
-> [Measuring What Makes You Unique: Difference-Aware User Modeling for Enhancing LLM Personalization](https://arxiv.org/abs/2503.02450)
+> [Measuring What Makes You Unique: Difference-Aware User Modeling for Enhancing LLM Personalization](https://huggingface.co/papers/2503.02450)
 > 
 > Yilun Qiu, Xiaoyan Zhao, Yang Zhang, Yimeng Bai, Wenjie Wang, Hong Cheng, Fuli Feng, Tat-Seng Chua
 
+[Code](https://github.com/SnowCharmQ/DPL)
+
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/67242d62055ce33014cf24d5/j2lG852s2qTk8A3bg9zVg.png)
 
 This dataset is an adaptation of the Amazon Reviews'23 dataset.  It contains user reviews for Books, CDs & Vinyl, and Movies & TV.  Each review includes user ID, profile information (ASIN, rating, text, timestamp, title), and the review data (ASIN, rating, text, timestamp, title).  The dataset is split into train, validation, and test sets for each category.