Add paper link, project page, and baseline code to dataset card

README.md

@@ -1,4 +1,7 @@
 ---
+license: cc-by-4.0
+task_categories:
+- other
 configs:
 - config_name: candidate
   data_files:
@@ -40,20 +43,18 @@ configs:
   data_files:
   - split: train
     path: mm_emb/emb_86_3584_1210_parquet/**/*.parquet
-license: cc-by-4.0
 ---
 
 # TencentGR-10M Dataset
 
-TAAC2025 Second Round Dataset(2025年腾讯广告算法大赛复赛数据集) TencentGR-10M Dataset is a large-scale, all-modality dataset designed specifically for generative recommendation (GR) in industrial advertising. Similar to [TencentGR-1M](https://huggingface.co/datasets/TAAC2025/TencentGR-1M), it is constructed from real, de-identified Tencent Ads logs, and aims to address the lack of realistic, public multi-modal datasets in the GR field.
-
-The main differences between TencentGR-10M and TencentGR-1M are:
-
-- Dataset Size: Provides **10 million** user sequences, with each user sequence containing up to 100 interacted items.
-- Labels: Each interaction within the sequence is explicitly labeled with **exposure(0)**, **click(1)**, and **conversion(2)** signals.
+[[Paper](https://huggingface.co/papers/2604.04976)] [[Project Page](https://algo.qq.com/2025)] [[Baseline Code](https://github.com/TencentAdvertisingAlgorithmCompetition/baseline_2025)]
 
+TAAC2025 Second Round Dataset (2025年腾讯广告算法大赛复赛数据集) TencentGR-10M Dataset is a large-scale, all-modality dataset designed specifically for generative recommendation (GR) in industrial advertising. Similar to [TencentGR-1M](https://huggingface.co/datasets/TAAC2025/TencentGR-1M), it is constructed from real, de-identified Tencent Ads logs, and aims to address the lack of realistic, public multi-modal datasets in the GR field.
 
+The main differences between TencentGR-10M and TencentGR-1M are:
 
+- **Dataset Size**: Provides **10 million** user sequences, with each user sequence containing up to 100 interacted items.
+- **Labels**: Each interaction within the sequence is explicitly labeled with **exposure(0)**, **click(1)**, and **conversion(2)** signals.
 
 ## Dataset Structure
 
@@ -92,7 +93,6 @@ Need to notice that we use two types of IDs in the dataset: the original IDs and
 
 The `item_feat` table contains the features of each item appeared in the `seq` set.
 
-<!-- 15 x 3 table: -->
 | **Field** | **Type** | **Description** | \# Non-None Values |
 |:---:|:---:|:---:|:---:|
 | `item_id` | int64 | RID for each item. |4783154 |
@@ -140,11 +140,6 @@ The `seq` table contains the behavior sequence for each user.
 
 The `candidate` table contains the candidate items for the competition.
 
-Note: 
-
-- This `candidate` is for the competition, but we do not provide the ground truth labels. People may refer to this format to build their own candidate set.
-- The `candidate` contains some items that are not in the `seq`.
-
 | **Field** | **Type** | **Description** | \# Non-None Values |
 |:---:|:---:|:---:|:---:|
 | `item_id` | int64 | OID for each item. | 660000 |
@@ -167,8 +162,6 @@ Note:
 
 The `mm_emb` tables contain the multimodal embeddings for each item. There are 6 different embedding dimensions(`[32, 1024, 3584, 4096, 3584, 3584]`) for 6 different embeddings(`[81, 82, 83, 84, 85, 86]`) placed in 6 files.
 
-Take the `81` embedding as an example:
-
 | **Field** | **Type** | **Description** | \# Non-None Values |
 |:---:|:---:|:---:|:---:|
 | `anonymous_cid` | string | OID for each item. | 4742961 |
@@ -176,39 +169,7 @@ Take the `81` embedding as an example:
 
 #### `indexer.pkl`
 
-This is a remapping file that maps the original IDs/Values to the remapped IDs/Values. The format is a dictionary with the following structure:
-
-```json
-{
-    "u": 
-        { 
-            OID: RID, 
-            ... 
-        },
-    "i": 
-        { 
-            OID: RID, 
-            ... 
-        },
-    "f":
-        {
-            101: 
-                {
-                    10100000000: 1, // original value: remapped value
-                    10100000001: 2, 
-                    ...
-                },
-            102:
-                {
-                    1020000000: 1,
-                    1020000001: 2,
-                    ...
-                },
-            ...
-        }
-}
-```
-
+This is a remapping file that maps the original IDs/Values to the remapped IDs/Values.
 
 ## Usage