Datasets:

TAAC2026
/

data_sample_1000

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)