RoryChou007/data_sample_1000

TAAC2026 Demo Dataset (1000 Samples)

A sample dataset containing 1000 user-item interaction records for the TAAC2026 competition.

Columns

Column	Type	Description
item_id	int64	Target item identifier.
item_feature	array[struct]	Array of target item feature dicts. Each element has feature_id, feature_value_type, and value fields (float_value, int_array, int_value).
label	array[struct]	Array of label dicts. Each element contains action_time and action_type.
seq_feature	struct	Sequence features dict with keys: action_seq, content_seq, item_seq. Each sub-key contains arrays of feature structs.
timestamp	int64	Event timestamp.
user_feature	array[struct]	Array of user feature dicts. Each element has feature_id, feature_value_type, and value fields (float_array, int_array, int_value).
user_id	string	User identifier.

Feature Struct Schema

Each feature element contains feature_id, feature_value_type, and several value fields. Depending on feature_value_type, the corresponding value fields are populated and the rest are null.

item_feature — value fields: int_value, float_value, int_array

{
  "feature_id": 6,
  "feature_value_type": "int_value",
  "float_value": null,
  "int_array": null,
  "int_value": 96,
}

user_feature — value fields: int_value, float_array, int_array

{
  "feature_id": 65,
  "feature_value_type": "int_value",
  "float_array": null,
  "int_array": null,
  "int_value": 19
}

seq_feature — value fields: int_array

{
  "feature_id": 19,
  "feature_value_type": "int_array",
  "int_array": [1, 1, 1, ...]
}

Possible "feature_value_type" values and their corresponding fields:

• "int_value" → int_value
• "float_value" → float_value
• "int_array" → int_array
• "float_array" → float_array
• Also there are some combinations of these types, e.g. "int_array_and_float_array" → both int_array and float_array are populated.

Label Schema

Each element in the label array:

{
  "action_time": 1770694299,
  "action_type": 1
}

Usage

import pandas as pd

df = pd.read_parquet("sample_data.parquet")
print(df.shape)       # (1000, 7)
print(df.columns)     # ['item_id', 'item_feature', 'label', 'seq_feature', 'timestamp', 'user_feature', 'user_id']

With Hugging Face datasets:

from datasets import load_dataset

ds = load_dataset("TAAC2026/data_sample_1000")
print(ds)