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, 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

Overview

Config Name	Path	Approx. Size	Description
candidate	candidate/	~97 MB	Candidate item set
item_feat	item_feat/	~348 MB	Item features
seq	seq/	~9.8 GB	User behavior sequences
user_feat	user_feat/	~88 MB	User features
mm_emb_81_32	mm_emb/emb_81_32_parquet/	~5.0 GB	Multimodal embedding (dim=32)
mm_emb_82_1024	mm_emb/emb_82_1024_parquet/	~36 GB	Multimodal embedding (dim=1024)
mm_emb_83_3584	mm_emb/emb_83_3584_parquet/	~116 GB	Multimodal embedding (dim=3584)
mm_emb_84_32	mm_emb/emb_84_32_parquet/	~3.7 GB	Multimodal embedding (dim=32)
mm_emb_85_3584	mm_emb/emb_85_3584_1210_parquet/	~116 GB	Multimodal embedding (dim=3584)
mm_emb_86_3584	mm_emb/emb_86_3584_1210_parquet/	~100 GB	Multimodal embedding (dim=3584)

Additional Files

File	Size	Description
indexer.pkl	~503 MB	Index mapping file (From original ID to remapped ID)

Data Format

All data files (except indexer.pkl) are stored in Snappy-compressed Parquet format.

Schema

For clarity and brevity, we provide detailed schema descriptions for each table below (Same as TencentGR-1M).

Need to notice that we use two types of IDs in the dataset: the original IDs and the remapped IDs, for simplicity, we will denote them as OID and RID. OIDs are used in mm_emb, and RIDs are used in all the training data and can be used for building models. The mapping between OIDs and RIDs can be found in the indexer.pkl file.

item_feat

The item_feat table contains the features of each item appeared in the seq set.

Field	Type	Description	# Non-None Values
item_id	int64	RID for each item.	4783154
100	int64	An encrypted feature.	4779045
101	int64	An encrypted feature.	4779045
102	int64	An encrypted feature.	4735917
112	int64	An encrypted feature.	4701740
114	int64	An encrypted feature.	4778327
115	int64	An encrypted feature.	1531415
116	int64	An encrypted feature.	4778146
117	int64	An encrypted feature.	4701740
118	int64	An encrypted feature.	4700703
119	int64	An encrypted feature.	4699894
120	int64	An encrypted feature.	4694982
121	int64	An encrypted feature.	4783154
122	int64	An encrypted feature.	4779045

user_feat

The user_feat table contains the features of each user appeared in the dataset.

Field	Type	Description	# Non-None Values
user_id	int64	RID for each user.	1001845
103	int64	An encrypted feature.	1000964
104	int64	An encrypted feature.	998043
105	int64	An encrypted feature.	859602
106	List[int64]	An encrypted feature.	880754
107	List[int64]	An encrypted feature.	387686
108	List[int64]	An encrypted feature.	170678
109	int64	An encrypted feature.	1001467
110	List[int64]	An encrypted feature.	430598

seq

The seq table contains the behavior sequence for each user.

Field	Type	Description	# Non-None Values
user_id	int64	RID for each user.	1001845
seq	List[Dict]	The behavior sequence for each user, each dict contains 3 keys: item_id(RID), action_type, and timestamp, where the values are all integers	1001845

candidate

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
retrieval_id	int64	The remapped ID for faiss retrieval (Start from 0).	660000
100	Dict[ "cold_start": int64, "feature_value": string ]	An encrypted feature.	659206
101	Dict[ "cold_start": int64, "feature_value": string ]	An encrypted feature.	659206
102	Dict[ "cold_start": int64, "feature_value": string ]	An encrypted feature.	653852
112	Dict[ "cold_start": int64, "feature_value": string ]	An encrypted feature.	654893
114	Dict[ "cold_start": int64, "feature_value": string ]	An encrypted feature.	659093
115	Dict[ "cold_start": int64, "feature_value": string ]	An encrypted feature.	195552
116	Dict[ "cold_start": int64, "feature_value": string ]	An encrypted feature.	659090
117	Dict[ "cold_start": int64, "feature_value": string ]	An encrypted feature.	654893
118	Dict[ "cold_start": int64, "feature_value": string ]	An encrypted feature.	654886
119	Dict[ "cold_start": int64, "feature_value": string ]	An encrypted feature.	654882
120	Dict[ "cold_start": int64, "feature_value": string ]	An encrypted feature.	654870
121	Dict[ "cold_start": int64, "feature_value": string ]	An encrypted feature.	660000
122	Dict[ "cold_start": int64, "feature_value": string ]	An encrypted feature.	659206

mm_emb

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
emb	List[ double ]	Embedding for each item.	4742961

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:

{
    "u": 
        { 
            OID: RID, 
            ... 
        },
    "i": 
        { 
            OID: RID, 
            ... 
        },
    "f":
        {
            101: 
                {
                    10100000000: 1, // original value: remapped value
                    10100000001: 2, 
                    ...
                },
            102:
                {
                    1020000000: 1,
                    1020000001: 2,
                    ...
                },
            ...
        }
}

Usage

from datasets import load_dataset

# Load a specific config
ds = load_dataset("TAAC2025/TencentGR-10M", name="candidate", split="train")

# Load item features
ds_item = load_dataset("TAAC2025/TencentGR-10M", name="item_feat", split="train")

# Load user behavior sequences
ds_seq = load_dataset("TAAC2025/TencentGR-10M", name="seq", split="train")

# Load user features
ds_user = load_dataset("TAAC2025/TencentGR-10M", name="user_feat", split="train")

# Load multimodal embeddings
ds_emb = load_dataset("TAAC2025/TencentGR-10M", name="mm_emb_81_32", split="train")