Improve dataset card: Add task categories, relevant tags, paper/code/project links, and update usage details

README.md

@@ -1,31 +1,40 @@
 ---
-license: apache-2.0
 language:
-  - en
-  - zh
-pretty_name: "AL-GR Raw Sequences 📜"
+- en
+- zh
+license: apache-2.0
+pretty_name: AL-GR Raw Sequences 📜
 tags:
-  - sequential-recommendation
-  - raw-data
-  - anonymized
-  - e-commerce
-  - next-item-prediction
+- sequential-recommendation
+- raw-data
+- anonymized
+- e-commerce
+- next-item-prediction
+- generative-retrieval
+- semantic-identifiers
+task_categories:
+- text-generation
+- text-retrieval
 ---
 
 # AL-GR/Origin-Sequence-Data: Raw User Behavior Sequences 📜
 
-## 📝 Dataset Summary
+[Paper](https://huggingface.co/papers/2509.20904) | [Project Page](https://huggingface.co/AL-GR) | [Code](https://github.com/selous123/al_sid)
+
+## About the Dataset
 
-This repository, `AL-GR/Origin-Sequence-Data`, contains the foundational **raw user behavior sequences** for the `AL-GR` ecosystem. It represents the data *before* it is formatted into the instruction-following prompts used for training Large Language Models (LLMs).
+This dataset is part of **FORGE**, a comprehensive benchmark for **FO**rming **R**aw user behavior sequences and **G**enerative r**E**trieval in Industrial Datasets, as presented in the paper [FORGE: Forming Semantic Identifiers for Generative Retrieval in Industrial Datasets](https://huggingface.co/papers/2509.20904). The FORGE benchmark aims to address challenges in semantic identifiers (SIDs) for generative retrieval (GR) by providing a large-scale public dataset with multimodal features.
 
-Each row in the dataset represents a step in a user's journey, consisting of a sequence of previously interacted items (`user_history`) and the next item they interacted with (`target_item`). All item IDs have been anonymized into short, unique strings.
+Specifically, this `AL-GR/Origin-Sequence-Data` repository contains the foundational **raw user behavior sequences** for the `AL-GR` ecosystem. It represents the data *before* it is formatted into the instruction-following prompts used for training Large Language Models (LLMs) in generative retrieval tasks. The full FORGE dataset comprises 14 billion user interactions and multimodal features of 250 million items sampled from Taobao, one of the biggest e-commerce platforms in China.
+
+Each row in this dataset (`Origin-Sequence-Data`) represents a step in a user's journey, consisting of a sequence of previously interacted items (`user_history`) and the next item they interacted with (`target_item`). All item IDs have been anonymized into short, unique strings.
 
 This dataset is ideal for:
-- 🧑‍🔬 Researchers who want to design their own data processing or prompting strategies.
+- 🧑‍🔬 Researchers who want to design their own data processing or prompting strategies for generative retrieval.
 - 📈 Training and evaluating traditional sequential recommendation models (e.g., GRU4Rec, SASRec, etc.).
-- 🔎 Understanding the source data from which the main `AL-GR` dataset was built.
+- 🔎 Understanding the source data from which the main `AL-GR` generative dataset was built.
 
-## 🚀 How to Use
+## 🚀 Sample Usage
 
 The data is structured in multiple folders (`s1_splits`, `s2_splits`, etc.), which is a non-standard format for the `datasets` library. To make loading seamless, a **loading script** is required.
 
@@ -40,11 +49,14 @@ import glob
 
 _DESCRIPTION = "Raw user behavior sequences for the AL-GR project, split into history and target item."
 _CITATION = """
-@misc{al-gr-origin-sequence,
-  author = {[Your Name or Team Name]},
-  title = {AL-GR/Origin-Sequence-Data: Raw User Behavior Sequences},
-  year = {[Year]},
-  # ... other citation info
+@misc{fu2025forgeformingsemanticidentifiers,
+      title={FORGE: Forming Semantic Semantic Identifiers for Generative Retrieval in Industrial Datasets}, 
+      author={Kairui Fu and Tao Zhang and Shuwen Xiao and Ziyang Wang and Xinming Zhang and Chenchi Zhang and Yuliang Yan and Junjun Zheng and Yu Li and Zhihong Chen and Jian Wu and Xiangheng Kong and Shengyu Zhang and Kun Kuang and Yuning Jiang and Bo Zheng},
+      year={2025},
+      eprint={2509.20904},
+      archivePrefix={arXiv},
+      primaryClass={cs.IR},
+      url={https://arxiv.org/abs/2509.20904}, 
 }
 """
 
@@ -118,7 +130,8 @@ dataset = load_dataset("AL-GR/Origin-Sequence-Data")
 print("Sample from s1 split:")
 print(dataset['s1'][0])
 
-print("\nSample from test split:")
+print("
+Sample from test split:")
 print(dataset['test'][0])
 ```
 
@@ -168,4 +181,4 @@ If you use this dataset in your research, please cite:
 
 ## 📜 License
 
-This dataset is licensed under the [e.g., Apache License 2.0].
+This dataset is licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0).