---
license: cc-by-nc-4.0
task_categories:
  - text-classification
tags:
  - TAAC2026
  - recommendation
size_categories:
  - n<1K
---

# TAAC2026 Demo Dataset 

> ⚠️ **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 and update some user/item features.
> Participants should refer to the updated `demo_1000.parquet` and this README for the latest schema and data details.


A sample dataset containing **1,000** 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 |
| **Format** | Apache Parquet |
| **File Size** | ~38.38 MB |

## Label Distribution

| `label_type` | Count | Percentage |
|---|---|---|
| 1 | 876 | 87.6% |
| 2 | 124 | 12.4% |

## Column Categories

The 120 columns fall into **6 categories**:

| Category | Count | Arrow 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)

| Column | Arrow Type | Nulls | Min | Max | Mean | Unique |
|---|---|---|---|---|---|---|
| `user_id` | `int64` | 0 | 2,727,076 | 12,728,427 | 7,835,799.34 | 1,000 |
| `item_id` | `int64` | 0 | 6,854 | 278,202,253 | 112,417,687.39 | 837 |
| `label_type` | `int32` | 0 | 1 | 2 | 1.124 | 2 |
| `label_time` | `int64` | 0 | 1,772,725,027 | 1,772,725,910 | 1,772,725,503.90 | 553 |
| `timestamp` | `int64` | 0 | 1,772,725,000 | 1,772,725,781 | 1,772,725,275.45 | 501 |

### User Int Features (46 columns)

#### Scalar Columns (`int64`)

| Column | Nulls | Null% | Min | Max | Mean | Unique |
|---|---|---|---|---|---|---|
| `user_int_feats_1` | 0 | 0.0% | 1 | 4 | 3.381 | 3 |
| `user_int_feats_3` | 30 | 3.0% | 9 | 1,839 | 987.557 | 341 |
| `user_int_feats_4` | 30 | 3.0% | 1 | 986 | 498.813 | 268 |
| `user_int_feats_48` | 2 | 0.2% | 3 | 99 | 58.006 | 52 |
| `user_int_feats_49` | 7 | 0.7% | 1 | 2 | 1.582 | 2 |
| `user_int_feats_50` | 4 | 0.4% | 0 | 1 | 0.998 | 2 |
| `user_int_feats_51` | 1 | 0.1% | 40 | 150 | 56.157 | 5 |
| `user_int_feats_52` | 1 | 0.1% | 5 | 174 | 93.856 | 36 |
| `user_int_feats_53` | 1 | 0.1% | 3 | 557 | 288.542 | 264 |
| `user_int_feats_54` | 368 | 36.8% | 3 | 2,843 | 1,476.783 | 462 |
| `user_int_feats_55` | 19 | 1.9% | 8 | 41 | 29.682 | 13 |
| `user_int_feats_56` | 19 | 1.9% | 1 | 1,434 | 752.658 | 405 |
| `user_int_feats_57` | 31 | 3.1% | 2 | 250 | 126.588 | 105 |
| `user_int_feats_58` | 150 | 15.0% | 1 | 2 | 1.699 | 2 |
| `user_int_feats_59` | 150 | 15.0% | 1 | 14 | 8.371 | 8 |
| `user_int_feats_82` | 204 | 20.4% | 1 | 23 | 9.097 | 23 |
| `user_int_feats_86` | 692 | 69.2% | 2 | 245 | 105.474 | 61 |
| `user_int_feats_92` | 494 | 49.4% | 1 | 2 | 1.500 | 2 |
| `user_int_feats_93` | 171 | 17.1% | 1 | 37 | 14.667 | 36 |
| `user_int_feats_94` | 521 | 52.1% | 1 | 6 | 3.770 | 6 |
| `user_int_feats_95` | 318 | 31.8% | 1 | 3 | 2.758 | 3 |
| `user_int_feats_96` | 678 | 67.8% | 1 | 3 | 1.817 | 3 |
| `user_int_feats_97` | 292 | 29.2% | 1 | 3 | 1.599 | 3 |
| `user_int_feats_98` | 103 | 10.3% | 1 | 3 | 2.678 | 3 |
| `user_int_feats_99` | 812 | 81.2% | 1 | 3 | 2.936 | 2 |
| `user_int_feats_100` | 845 | 84.5% | 1 | 2 | 1.955 | 2 |
| `user_int_feats_101` | 910 | 91.0% | 2 | 3 | 2.956 | 2 |
| `user_int_feats_102` | 877 | 87.7% | 1 | 3 | 1.130 | 2 |
| `user_int_feats_103` | 862 | 86.2% | 1 | 3 | 2.717 | 3 |
| `user_int_feats_104` | 372 | 37.2% | 1 | 3 | 2.360 | 3 |
| `user_int_feats_105` | 309 | 30.9% | 1 | 3 | 2.287 | 3 |
| `user_int_feats_106` | 160 | 16.0% | 1 | 3 | 1.760 | 3 |
| `user_int_feats_107` | 300 | 30.0% | 1 | 2 | 1.094 | 2 |
| `user_int_feats_108` | 516 | 51.6% | 2 | 7 | 5.455 | 6 |
| `user_int_feats_109` | 854 | 85.4% | 1 | 7 | 2.993 | 7 |

#### Array Columns (`list<int64>`)

| Column | Nulls | Null% | Element Type |
|---|---|---|---|
| `user_int_feats_15` | 139 | 13.9% | `list<int64>` |
| `user_int_feats_60` | 592 | 59.2% | `list<int64>` |
| `user_int_feats_62` | 70 | 7.0% | `list<int64>` |
| `user_int_feats_63` | 70 | 7.0% | `list<int64>` |
| `user_int_feats_64` | 70 | 7.0% | `list<int64>` |
| `user_int_feats_65` | 80 | 8.0% | `list<int64>` |
| `user_int_feats_66` | 86 | 8.6% | `list<int64>` |
| `user_int_feats_80` | 200 | 20.0% | `list<int64>` |
| `user_int_feats_89` | 55 | 5.5% | `list<int64>` |
| `user_int_feats_90` | 91 | 9.1% | `list<int64>` |
| `user_int_feats_91` | 450 | 45.0% | `list<int64>` |

### User Dense Features (10 columns)

All columns are `list<float>` arrays (e.g. embedding vectors).

| Column | Nulls | Null% | Description |
|---|---|---|---|
| `user_dense_feats_61` | 2 | 0.2% | 256-dim embedding vector |
| `user_dense_feats_62` | 70 | 7.0% | Variable-length float array |
| `user_dense_feats_63` | 70 | 7.0% | Variable-length float array |
| `user_dense_feats_64` | 70 | 7.0% | Variable-length float array |
| `user_dense_feats_65` | 80 | 8.0% | Variable-length float array |
| `user_dense_feats_66` | 86 | 8.6% | Variable-length float array |
| `user_dense_feats_87` | 15 | 1.5% | 320-dim embedding vector |
| `user_dense_feats_89` | 55 | 5.5% | Variable-length float array |
| `user_dense_feats_90` | 91 | 9.1% | Variable-length float array |
| `user_dense_feats_91` | 450 | 45.0% | Variable-length float array |

### Item Int Features (14 columns)

| Column | Arrow Type | Nulls | Null% | Min | Max | Mean | Unique |
|---|---|---|---|---|---|---|---|
| `item_int_feats_5` | `int64` | 2 | 0.2% | 4 | 325 | 118.452 | 82 |
| `item_int_feats_6` | `int64` | 2 | 0.2% | 0 | 977 | 419.073 | 216 |
| `item_int_feats_7` | `int64` | 2 | 0.2% | 0 | 2,806 | 1,052.866 | 349 |
| `item_int_feats_8` | `int64` | 2 | 0.2% | -1 | 2,431 | 463.712 | 226 |
| `item_int_feats_9` | `int64` | 2 | 0.2% | 3 | 37 | 21.171 | 24 |
| `item_int_feats_10` | `int64` | 2 | 0.2% | 2 | 309 | 150.007 | 110 |
| `item_int_feats_11` | `list<int64>` | 439 | 43.9% | — | — | — | — |
| `item_int_feats_12` | `int64` | 2 | 0.2% | 0 | 2,777 | 1,039.381 | 352 |
| `item_int_feats_13` | `int64` | 2 | 0.2% | 1 | 8 | 4.457 | 8 |
| `item_int_feats_16` | `int64` | 2 | 0.2% | 2 | 35,259 | 12,356.101 | 662 |
| `item_int_feats_81` | `int64` | 2 | 0.2% | 0 | 2 | 0.508 | 3 |
| `item_int_feats_83` | `int64` | 832 | 83.2% | 1 | 31 | 17.595 | 22 |
| `item_int_feats_84` | `int64` | 832 | 83.2% | 3 | 226 | 131.131 | 66 |
| `item_int_feats_85` | `int64` | 832 | 83.2% | 4 | 1,001 | 439.816 | 103 |

### Domain Sequence Features (45 columns)

Variable-length `list<int64>` sequences from 4 behavioral domains:

| Domain | Columns | Count | Nulls per Col | Max Seq Length |
|---|---|---|---|---|
| **domain_a** | `domain_a_seq_38` – `_46` | 9 | 5 | 1,888 |
| **domain_b** | `domain_b_seq_67` – `_79`, `_88` | 14 | 12 | 1,952 |
| **domain_c** | `domain_c_seq_27` – `_37`, `_47` | 12 | 2 | 3,894 |
| **domain_d** | `domain_d_seq_17` – `_26` | 10 | 80 | 3,951 |

---

## Null Coverage Summary

| Group | Columns | Zero Coverage | Low Coverage (<50%) | Notes |
|---|---|---|---|---|
| `user_int_feats_` | 46 | 0 | 11 | Columns 99–103, 109 have >80% nulls |
| `user_dense_feats_` | 10 | 0 | 0 | `user_dense_feats_91` has 45% nulls |
| `item_int_feats_` | 14 | 0 | 3 | `item_int_feats_83`–`85` have ~83% nulls |
| `domain_a_seq_` | 9 | 0 | 0 | Very low null rate (0.5%) |
| `domain_b_seq_` | 14 | 0 | 0 | Low null rate (1.2%) |
| `domain_c_seq_` | 12 | 0 | 0 | Very low null rate (0.2%) |
| `domain_d_seq_` | 10 | 0 | 0 | Moderate null rate (8.0%) |

### High-Null Columns (>50% null)

| Column | Null Count | Null% |
|---|---|---|
| `user_int_feats_101` | 910 | 91.0% |
| `user_int_feats_102` | 877 | 87.7% |
| `user_int_feats_103` | 862 | 86.2% |
| `user_int_feats_109` | 854 | 85.4% |
| `user_int_feats_100` | 845 | 84.5% |
| `item_int_feats_83` | 832 | 83.2% |
| `item_int_feats_84` | 832 | 83.2% |
| `item_int_feats_85` | 832 | 83.2% |
| `user_int_feats_99` | 812 | 81.2% |
| `user_int_feats_86` | 692 | 69.2% |
| `user_int_feats_96` | 678 | 67.8% |
| `user_int_feats_60` | 592 | 59.2% |
| `user_int_feats_94` | 521 | 52.1% |
| `user_int_feats_108` | 516 | 51.6% |
| `user_int_feats_92` | 494 | 49.4% |
| `user_dense_feats_91` | 450 | 45.0% |
| `user_int_feats_91` | 450 | 45.0% |
| `item_int_feats_11` | 439 | 43.9% |

---

## Usage

```python
import pyarrow.parquet as pq
import pandas as pd

# Read the parquet file
pf = pq.ParquetFile("data_1000/demo_1000.parquet")
table = pf.read()
df = table.to_pandas()

print(df.shape)       # (1000, 120)
print(df.columns)     # ['user_id', 'item_id', 'label_type', ...]
```

```python
# Check label distribution
print(df['label_type'].value_counts())
# 1    876
# 2    124

# Access a sequence feature
seq = df['domain_a_seq_38'].dropna().iloc[0]
print(type(seq), len(seq))  # <class 'numpy.ndarray'> variable length

# Access an embedding feature
emb = df['user_dense_feats_61'].dropna().iloc[0]
print(type(emb), len(emb))  # <class 'numpy.ndarray'> 256
```

```python
# Null analysis
null_pct = df.isnull().mean().sort_values(ascending=False)
print(null_pct[null_pct > 0.5])  # Columns with >50% nulls
```

## Relationship to Other Files

| File | Rows | Size | Compression | Description |
|---|---|---|---|---|
| `data_1000/demo_1000.parquet` | 1,000 | ~38 MB | None | **This dataset** — first 1,000 rows |
| `demo_data/demo_1000_0408.gz.parquet` | 1,016 | ~27 MB | Gzip | Full 1,016-row source dataset |
| `test_demo_data/sample_10.parquet` | 10 | ~548 KB | — | 10-row test sample |

## Key Notes

1. **Nullable int64**: All `*_int_feats_*` scalar columns are stored as Arrow `int64` with native null support. When reading with pandas, nullable int columns may be converted to `float64` — use `df[col].fillna(-1).astype(int)` or read with `pd.Int64Dtype()` to preserve the integer type.
2. **No nested structs**: Unlike the older `sample_data.parquet`, all features are flat top-level columns.
3. **Sparse features**: 18 columns have >40% null values — handle missing data carefully during feature engineering.
4. **Sequence lengths vary widely**: Domain sequences range from length 1 to ~3,951, which may require truncation or padding for model input.
5. **Imbalanced labels**: ~87.6% label_type=1 vs ~12.4% label_type=2 — consider class balancing strategies.