Duplicate from TAAC2026/data_sample_1000

Co-authored-by: Yuliang <cLinuss@users.noreply.huggingface.co>

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README.md

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+---
+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)
+```

sample_data.parquet

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