Datasets:

zjt24
/

data_sample_1000

@@ -1,153 +0,0 @@
-{
-  "source_dataset": "TAAC2026/data_sample_1000",
-  "source_file": "demo_1000.parquet",
-  "source_rows": 1000,
-  "source_columns": 120,
-  "converted_layout": "scalar event table plus per-family sequence tables",
-  "time_mapping": {
-    "scalar_table": "Time = timestamp * 1000 (epoch milliseconds)",
-    "sequence_tables": "Time = sequence_index within each source interaction event"
-  },
-  "tag_columns": {
-    "scalar_table": [
-      "event_index",
-      "user_id"
-    ],
-    "sequence_tables": [
-      "event_index",
-      "user_id",
-      "item_id"
-    ]
-  },
-  "scalar_table": {
-    "table_name": "data_sample_1000_scalar",
-    "rows": 1000,
-    "parquet": "data_sample_1000_scalar.parquet",
-    "tsfile": "data/data_sample_1000_scalar.tsfile"
-  },
-  "sequence_tables": {
-    "user_int_lists": {
-      "columns": [
-        "user_int_feats_15",
-        "user_int_feats_60",
-        "user_int_feats_62",
-        "user_int_feats_63",
-        "user_int_feats_64",
-        "user_int_feats_65",
-        "user_int_feats_66",
-        "user_int_feats_80",
-        "user_int_feats_89",
-        "user_int_feats_90",
-        "user_int_feats_91"
-      ],
-      "rows": 11560,
-      "parquet": "data_sample_1000_user_int_lists.parquet",
-      "tsfile": "data/data_sample_1000_user_int_lists.tsfile"
-    },
-    "user_dense_lists": {
-      "columns": [
-        "user_dense_feats_61",
-        "user_dense_feats_62",
-        "user_dense_feats_63",
-        "user_dense_feats_64",
-        "user_dense_feats_65",
-        "user_dense_feats_66",
-        "user_dense_feats_87",
-        "user_dense_feats_89",
-        "user_dense_feats_90",
-        "user_dense_feats_91"
-      ],
-      "rows": 318538,
-      "parquet": "data_sample_1000_user_dense_lists.parquet",
-      "tsfile": "data/data_sample_1000_user_dense_lists.tsfile"
-    },
-    "item_lists": {
-      "columns": [
-        "item_int_feats_11"
-      ],
-      "rows": 2086,
-      "parquet": "data_sample_1000_item_lists.parquet",
-      "tsfile": "data/data_sample_1000_item_lists.tsfile"
-    },
-    "domain_a_seq": {
-      "columns": [
-        "domain_a_seq_38",
-        "domain_a_seq_39",
-        "domain_a_seq_40",
-        "domain_a_seq_41",
-        "domain_a_seq_42",
-        "domain_a_seq_43",
-        "domain_a_seq_44",
-        "domain_a_seq_45",
-        "domain_a_seq_46"
-      ],
-      "rows": 701086,
-      "parquet": "data_sample_1000_domain_a_seq.parquet",
-      "tsfile": "data/data_sample_1000_domain_a_seq.tsfile"
-    },
-    "domain_b_seq": {
-      "columns": [
-        "domain_b_seq_67",
-        "domain_b_seq_68",
-        "domain_b_seq_69",
-        "domain_b_seq_70",
-        "domain_b_seq_71",
-        "domain_b_seq_72",
-        "domain_b_seq_73",
-        "domain_b_seq_74",
-        "domain_b_seq_75",
-        "domain_b_seq_76",
-        "domain_b_seq_77",
-        "domain_b_seq_78",
-        "domain_b_seq_79",
-        "domain_b_seq_88"
-      ],
-      "rows": 570758,
-      "parquet": "data_sample_1000_domain_b_seq.parquet",
-      "tsfile": "data/data_sample_1000_domain_b_seq.tsfile"
-    },
-    "domain_c_seq": {
-      "columns": [
-        "domain_c_seq_27",
-        "domain_c_seq_28",
-        "domain_c_seq_29",
-        "domain_c_seq_30",
-        "domain_c_seq_31",
-        "domain_c_seq_32",
-        "domain_c_seq_33",
-        "domain_c_seq_34",
-        "domain_c_seq_35",
-        "domain_c_seq_36",
-        "domain_c_seq_37",
-        "domain_c_seq_47"
-      ],
-      "rows": 449431,
-      "parquet": "data_sample_1000_domain_c_seq.parquet",
-      "tsfile": "data/data_sample_1000_domain_c_seq.tsfile"
-    },
-    "domain_d_seq": {
-      "columns": [
-        "domain_d_seq_17",
-        "domain_d_seq_18",
-        "domain_d_seq_19",
-        "domain_d_seq_20",
-        "domain_d_seq_21",
-        "domain_d_seq_22",
-        "domain_d_seq_23",
-        "domain_d_seq_24",
-        "domain_d_seq_25",
-        "domain_d_seq_26"
-      ],
-      "rows": 1099859,
-      "parquet": "data_sample_1000_domain_d_seq.parquet",
-      "tsfiles": [
-        "data/data_sample_1000_domain_d_seq_1.tsfile",
-        "data/data_sample_1000_domain_d_seq_2.tsfile"
-      ]
-    }
-  },
-  "renamed_columns": {
-    "timestamp": "event_timestamp"
-  },
-  "dropped_columns": []
-}

meta/source_README.md DELETED Viewed

@@ -1,107 +0,0 @@
----
-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)
-```