TAAC2026/second_round_sample_1000

TAAC2026 Second Round Demo Dataset (1000 Samples)

A sample dataset containing 1000 user-item interaction records for the second round of TAAC2026 competition. This dataset uses a flat column layout — all features are stored as individual top-level columns instead of nested structs/arrays.

Dataset Overview

Property	Value
File	demo_1000.parquet
Rows	1,000
Columns	142
File Size	~40 MB

Columns

The 142 columns fall into 7 categories:

Category	Count	Arrow Type	Description
ID & Label	5	int64 / int32	Core identifiers, label, and timestamp
User Int Features	54	int64 / list<int64>	Discrete user features, including both single-value scalar features (such as age, gender, etc.) and multi-value array features (like marital status, etc.), describing user basic attributes and preferences.
User Dense Features	17	list<float>	Continuous-valued user features, including embeddings and other aligned signals for some corresponding integer features.
Item Int Features	17	int64 / list<int64>	Discrete item features, including item categories, types, and other basic information, as well as multi-label information for items.
Item Dense Features	4	list<float>	Continuous-valued item features, including embeddings.
Domain Sequence Features	45	list<int64>	Behavioral sequence features from 4 domains

Detailed Column Schema

ID & Label Columns (5 columns)

All these 5 columns have no null value.

Column	user_id	item_id	label_type	label_time	timestamp
Data Type	int64	int64	int32	int64	int64

User Int Features (54 columns)

• user_int_feats_{1,3,4,48-59,82,86,92-110}: Scalar int64, total 36 columns.
• user_int_feats_{15, 60, 62-66, 80, 89-91, 111, 115-118, 121-122}: Array list<int64>, total 18 columns.

User Dense Features (17 columns)

• user_dense_feats_{61, 87, 120, 123, 130-132}: Array list<float>, total 7 columns, representing user embedding features(SUM , LFM4Ads ).
• user_dense_feats_{62-66, 89-91, 118, 121}: Array list<float>, total 10 columns, corresponding to the integer features user_int_feats_{62-66, 89-91, 118, 121}, with the same length.

  • An Example: user_int_feats_62: [1, 2, 3], user_dense_feats_62: [10.5, 20, 15.5]

    Explanation: Here, the two arrays are aligned element by element. For example, the 1st element in user_int_feats_62 (value 1) denotes a specific entity or category, while the 1st element in user_dense_feats_62 (value 10.5) provides some statistics for that element, such as a dwell time, a score/probability.

Item Int Features (17 columns)

• item_int_feats_{5-10, 12-13, 16, 81, 83-85, 114, 119, 126}: Scalar int64, total 16 columns.
• item_int_feats_{11}: Array list<int64>, total 1 column.

Item Dense Features (4 columns)

• item_dense_feats_{124, 127-129}: Array list<float>, total 4 columns, representing item embedding features.

Domain Sequence Features (45 columns)

list<int64> sequences from 4 behavioral domains:

• domain_a_seq_{38-46}: 9 columns
• domain_b_seq_{67-79, 88}: 14 columns
• domain_c_seq_{27-37, 47}: 12 columns
• domain_d_seq_{17-26}: 10 columns

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Usage

import pyarrow.parquet as pq
import pandas as pd

# Read the parquet file
df = pd.read_parquet("demo_1000.parquet")

print(df.shape)       # (1000, 142)
print(df.columns)     # ['user_id', 'item_id', 'label_type', ...]

With Hugging Face datasets:

from datasets import load_dataset

ds = load_dataset("TAAC2026/second_round_sample_1000")
print(ds)