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TAAC2025/TencentGR-1M

TencentGR-1M Dataset Paper | Project Page | Code TAAC2025 Preliminary Round Dataset (2025年腾讯广告算法大赛初赛数据集) TencentGR-1M Dataset is a large-scale, all-modality dataset designed specifically for generative recommendation (GR) in industrial advertising. Constructed from real, de-identified Tencent Ads logs, it aims to address the lack of realistic, public multi-modal datasets in the GR field. Data Features: Contains rich collaborative IDs and multi-modal representations (text and… See the full description on the dataset page: https://huggingface.co/datasets/TAAC2025/TencentGR-1M.

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Dataset Card

TencentGR-1M Dataset

**Paper** | **Project Page** | **Code**

TAAC2025 Preliminary Round Dataset (2025年腾讯广告算法大赛初赛数据集) TencentGR-1M Dataset is a large-scale, all-modality dataset designed specifically for generative recommendation (GR) in industrial advertising. Constructed from real, de-identified Tencent Ads logs, it aims to address the lack of realistic, public multi-modal datasets in the GR field.

—Data Features: Contains rich collaborative IDs and multi-modal representations (text and vision) extracted using state-of-the-art embedding models.
—Dataset Size: Provides 1 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) and click(1) signals.

Dataset Structure

Overview

Config Name	Path	Approx. Size	Description
`candidate`	`candidate/`	~22 MB	Candidate item set
`item_feat`	`item_feat/`	~104 MB	Item features
`seq`	`seq/`	~881 MB	User behavior sequences
`user_feat`	`user_feat/`	~8.4 MB	User features
`mm_emb_81_32`	`mm_emb/emb_81_32_parquet/`	~901 MB	Multimodal embedding (dim=32)
`mm_emb_82_1024`	`mm_emb/emb_82_1024_parquet/`	~9.4 GB	Multimodal embedding (dim=1024)
`mm_emb_83_3584`	`mm_emb/emb_83_3584_parquet/`	~31 GB	Multimodal embedding (dim=3584)
`mm_emb_84_4096`	`mm_emb/emb_84_4096_parquet/`	~30 GB	Multimodal embedding (dim=4096)
`mm_emb_85_3584`	`mm_emb/emb_85_3584_parquet/`	~31 GB	Multimodal embedding (dim=3584)
`mm_emb_86_3584`	`mm_emb/emb_86_3584_parquet/`	~26 GB	Multimodal embedding (dim=3584)

Additional Files

File	Size	Description
`indexer.pkl`	~142 MB	Index mapping file (From original ID to remapped ID)

Data Format

All data files are stored in Snappy-compressed Parquet format.

Schema

For clarity and brevity, we provide detailed schema descriptions for each table below.

Note that we use two types of IDs in the dataset: the original IDs (OID) and the remapped IDs (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.

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\[ "cold\start": int64, "featurevalue": string \]	An encrypted feature.	659206
`101`	Dict\[ "cold\start": int64, "featurevalue": string \]	An encrypted feature.	659206
`102`	Dict\[ "cold\start": int64, "featurevalue": string \]	An encrypted feature.	653852
`112`	Dict\[ "cold\start": int64, "featurevalue": string \]	An encrypted feature.	654893
`114`	Dict\[ "cold\start": int64, "featurevalue": string \]	An encrypted feature.	659093
`115`	Dict\[ "cold\start": int64, "featurevalue": string \]	An encrypted feature.	195552
`116`	Dict\[ "cold\start": int64, "featurevalue": string \]	An encrypted feature.	659090
`117`	Dict\[ "cold\start": int64, "featurevalue": string \]	An encrypted feature.	654893
`118`	Dict\[ "cold\start": int64, "featurevalue": string \]	An encrypted feature.	654886
`119`	Dict\[ "cold\start": int64, "featurevalue": string \]	An encrypted feature.	654882
`120`	Dict\[ "cold\start": int64, "featurevalue": string \]	An encrypted feature.	654870
`121`	Dict\[ "cold\start": int64, "featurevalue": string \]	An encrypted feature.	660000
`122`	Dict\[ "cold\start": int64, "featurevalue": string \]	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]).

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.

Usage

python

from datasets import load_dataset

# Load a specific config
ds = load_dataset("TAAC2025/TencentGR-1M", name="candidate", split="train")

# Load item features
ds_item = load_dataset("TAAC2025/TencentGR-1M", name="item_feat", split="train")

# Load user behavior sequences
ds_seq = load_dataset("TAAC2025/TencentGR-1M", name="seq", split="train")

# Load user features
ds_user = load_dataset("TAAC2025/TencentGR-1M", name="user_feat", split="train")

# Load multimodal embeddings
ds_emb = load_dataset("TAAC2025/TencentGR-1M", name="mm_emb_81_32", split="train")