README.md

metadata

license: cc-by-nc-4.0
tags:
  - TAAC2026
  - recommendation

TAAC2026 Demo Dataset (1000 Samples)

⚠️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. 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

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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: 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

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:

from datasets import load_dataset

ds = load_dataset("TAAC2026/data_sample_1000")
print(ds)