| --- |
| license: cc-by-nc-4.0 |
| tags: |
| - TAAC2026 |
| - recommendation |
| --- |
| |
| # TAAC2026 Demo Dataset (1000 Samples) |
|
|
|
|
| > [!WARNING] ⚠️**Update[2026.04.10]:** |
| > This demo dataset has been updated to newest version with the following changes: |
| > - The parquet file is now a **flat column layout**, with all features as top-level columns. |
| > - Add a sequence feature, rename feature names and update some features. |
| > Participants should refer to the updated `demo_1000.parquet` and this `README.md` for the latest schema and data details. |
| |
| |
| A sample dataset containing 1000 user-item interaction records for the [TAAC2026 competition](https://algo.qq.com/). 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** | 120 | |
| | **File Size** | ~39 MB | |
|
|
| ## Columns |
|
|
| The 120 columns fall into **6 categories**: |
|
|
| | Category | Count | Data Type | Description | |
| |---|---|---|---| |
| | **ID & Label** | 5 | `int64` / `int32` | Core identifiers, label, and timestamp | |
| | **User Int Features** | 46 | `int64` / `list<int64>` | Integer-valued user features (scalar or array) | |
| | **User Dense Features** | 10 | `list<float>` | Float-array user features | |
| | **Item Int Features** | 14 | `int64` / `list<int64>` | Integer-valued item features (scalar or array) | |
| | **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 | Data Type | |
| |---|---| |
| | `user_id` | `int64` | |
| | `item_id` | `int64` | |
| | `label_type` | `int32` | |
| | `label_time` | `int64` | |
| | `timestamp` | `int64` | |
|
|
| > [!NOTE] **Note:** |
| > When `user_int_feats_{fid}` and `user_dense_feats_{fid}` share the same `{fid}`, they are aligned and jointly describe the same entity or signal. |
|
|
| ### User Int Features (46 columns) |
|
|
| - `user_int_feats_{1,3,4,48-59,82,86,92-109}`: Scalar `int64`, total 35 columns. |
| - `user_int_feats_{15, 60, 62-66, 80, 89-91}`: Array `list<int64>`, total 11 columns. |
|
|
|
|
| ### User Dense Features (10 columns) |
|
|
| - `user_dense_feats_{61-66, 87, 89-91}`: Array `list<float>`, total 10 columns. |
|
|
|
|
| ### Item Int Features (14 columns) |
|
|
| - `item_int_feats_{5-10, 12-13, 16, 81, 83-85}`: Scalar `int64`, total 13 columns. |
| - `item_int_feats_{11}`: Array `list<int64>`, total 1 column. |
|
|
|
|
| ### 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 |
|
|
| --- |
|
|
| ## Usage |
|
|
| ```python |
| import pyarrow.parquet as pq |
| import pandas as pd |
| |
| # Read the parquet file |
| df = pd.read_parquet("demo_1000.parquet") |
| |
| print(df.shape) # (1000, 120) |
| print(df.columns) # ['user_id', 'item_id', 'label_type', ...] |
| ``` |
|
|
| With Hugging Face `datasets`: |
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("TAAC2026/data_sample_1000") |
| print(ds) |
| ``` |
|
|
