File size: 2,791 Bytes
d07a6ee af7afd2 d07a6ee af7afd2 d07a6ee | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 | ---
license: cc-by-nc-4.0
---
# TAAC2026 Demo Dataset (1000 Samples)
A sample dataset containing 1000 user-item interaction records for the [TAAC2026 competition](https://algo.qq.com).
## Dataset Description
- **Rows**: 1,000
- **Format**: Parquet (`sample_data.parquet`)
- **File Size**: ~68 MB
## 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`
```json
{
"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`
```json
{
"feature_id": 65,
"feature_value_type": "int_value",
"float_array": null,
"int_array": null,
"int_value": 19
}
```
**`seq_feature`** — value fields: `int_array`
```json
{
"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:
```json
{
"action_time": 1770694299,
"action_type": 1
}
```
## Usage
```python
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`:
```python
from datasets import load_dataset
ds = load_dataset("TAAC2026/data_sample_1000")
print(ds)
```
|