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Add dataset rec tmall 2014 #141

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7a62a23
feat[conversion_tools]: Add the TMALL2014Dataset class and its data p…
0gaowei Oct 4, 2025
61e3840
feat[log]: Add a new logging module to enhance the logging function o…
0gaowei Oct 4, 2025
19b64e2
feat: add datasets info and usage docs of Tmall2014
0gaowei Oct 4, 2025
ab81b55
feat: update interaction type handling in TMALL2014Dataset and docume…
0gaowei Oct 19, 2025
9d94fd0
feat: add TaobaoDataset class and usage documentation
0gaowei Oct 19, 2025
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5 changes: 5 additions & 0 deletions .gitignore
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__pycache__/
*/__pycache__/
**/__pycache__/
*.pyc
conversion_tools/logs/
37 changes: 20 additions & 17 deletions README.md
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Expand Up @@ -48,6 +48,8 @@ This dataset is a collection of anonymized customer sessions containing products
- Yelp-full: This is a combination dataset including four versions of yelp datasets mentioned above, where the duplicates are dropped and the number of total reviews is 28,908,240.
- [Tmall](https://tianchi.aliyun.com/dataset/dataDetail?dataId=53):
This dataset is provided by Ant Financial Services, using in the IJCAI16 contest.
- [Tmall2014](https://tianchi.aliyun.com/dataset/140281):
This is a large-scale e-commerce dataset from Tmall.com containing user behavior logs from 2013. The dataset includes multiple types of user-item interactions: clicks, add-to-cart, favorites (collect), and purchases (alipay).
- [DIGINETICA](https://competitions.codalab.org/competitions/11161):
The dataset includes user sessions extracted from an e-commerce search engine logs, with anonymized user ids,
hashed queries, hashed query terms, hashed product descriptions and meta-data, log-scaled prices, clicks, and purchases.
Expand Down Expand Up @@ -204,23 +206,24 @@ These datasets contain measurements of clothing fit from [RentTheRunway](https:/
| 17 | [Ta Feng](https://github.com/RUCAIBox/RecommenderSystems-Datasets/tree/master/dataset_info/TaFeng) | 32,266 | 23,812 | 817,741 | 99\.89% | Click | √ | √ | √ | √ |
| 18 | [Foursquare](https://github.com/RUCAIBox/RecommenderSystems-Datasets/tree/master/dataset_info/Foursquare) | \- | \- | \- | \- | Check-in | √ | | √ | |
| 19 | [Tmall](https://github.com/RUCAIBox/RecommenderSystems-Datasets/tree/master/dataset_info/Tmall) | 963,923 | 2,353,207 | 44,528,127 | 99.99% | Click/Buy | √ | | | √ |
| 20 | [YOOCHOOSE](https://github.com/RUCAIBox/RecommenderSystems-Datasets/tree/master/dataset_info/YOOCHOOSE) | 9,249,729 | 52,739 | 34,154,697 | 99.99% | Click/Buy | √ | | | √ |
| 21 | [Retailrocket](https://github.com/RUCAIBox/RecommenderSystems-Datasets/tree/master/dataset_info/Retailrocket) | 1,407,580 | 247,085 | 2,756,101 | 99.99% | View/Addtocart/Transaction | √ | | | |
| 22 | [LFM-1b](https://github.com/RUCAIBox/RecommenderSystems-Datasets/tree/master/dataset_info/LFM-1b) | 120,322 | 3,123,496 | 1,088,161,692 | 99\.71% | Click | √ | √ | √ | √ |
| 23 | [MIND](https://github.com/RUCAIBox/RecommenderSystems-Datasets/tree/master/dataset_info/MIND) | - | - | - | - | Click | √ | | | |
| 24 | BeerAdvocate | 33,388 | 66,055 | 1,586,614 | 99\.9281% | Rating<br/> \[0,5\] | √ | | √ | |
| 25 | Behance | 63,497 | 178,788 | 1,000,000 | 99\.9912% | Likes | √ | | √ | |
| 26 | DianPing | 542,706 | 243,247 | 4,422,473 | 99\.9967% | Rating<br/> \[0,5\] | √ | | √ | √ |
| 27 | EndoMondo | 1,104 | 253,020 | 253,020 | 99\.9094% | Workout Logs | √ | √ | | √ |
| 28 | Food | 226,570 | 231,637 | 1,132,367 | 99\.9978% | Rating<br/> \[0,5\] | √ | | √ | |
| 29 | GoodReads | 876,145 | 2,360,650 | 228,648,342 | 99\.9889% | Rating<br/> \[0,5\] | √ | | √ | |
| 30 | [KGRec](https://github.com/RUCAIBox/RecommenderSystems-Datasets/tree/master/dataset_info/KGRec) | - | - | - | - | Click | | | √ | |
| 31 | ModCloth | 47,958 | 1,378 | 82,790 | 99\.8747% | Rating<br/> \[0,5\] | | √ | √ | √ |
| 32 | RateBeer | 29,265 | 110,369 | 2,924,163 | 99\.9095% | Overall Rating<br/> \[0,20\] | √ | | √ | √ |
| 33 | RentTheRunway | 105,571 | 5,850 | 192,544 | 99\.9688% | Rating<br/> \[0,10\] | √ | √ | √ | √ |
| 34 | [Twitch](https://github.com/RUCAIBox/RecommenderSystems-Datasets/tree/master/dataset_info/Twitch) | 15,524,309 | 6,161,666 | 474,676,929 | 99\.9995% | Click | | | | √ |
| 35 | Amazon_M2 | 3,606,349 | 1,410,675 | 15,306,183 | \- | Click | | | √ | √ |
| 36 | Music4All-Onion | 119,140 | 109,269 | 252,984,396 | \- | Click | √ | | √ | √ |
| 20 | [Tmall2014](dataset_info/Tmall2014) | ~1,500,000 | ~8,000,000 | ~22,400,000 (click) | 99.99% | Click/Cart/Collect/Alipay | √ | | | |
| 21 | [YOOCHOOSE](https://github.com/RUCAIBox/RecommenderSystems-Datasets/tree/master/dataset_info/YOOCHOOSE) | 9,249,729 | 52,739 | 34,154,697 | 99.99% | Click/Buy | √ | | | √ |
| 22 | [Retailrocket](https://github.com/RUCAIBox/RecommenderSystems-Datasets/tree/master/dataset_info/Retailrocket) | 1,407,580 | 247,085 | 2,756,101 | 99.99% | View/Addtocart/Transaction | √ | | | |
| 23 | [LFM-1b](https://github.com/RUCAIBox/RecommenderSystems-Datasets/tree/master/dataset_info/LFM-1b) | 120,322 | 3,123,496 | 1,088,161,692 | 99\.71% | Click | √ | √ | √ | √ |
| 24 | [MIND](https://github.com/RUCAIBox/RecommenderSystems-Datasets/tree/master/dataset_info/MIND) | - | - | - | - | Click | √ | | | |
| 25 | BeerAdvocate | 33,388 | 66,055 | 1,586,614 | 99\.9281% | Rating<br/> \[0,5\] | √ | | √ | |
| 26 | Behance | 63,497 | 178,788 | 1,000,000 | 99\.9912% | Likes | √ | | √ | |
| 27 | DianPing | 542,706 | 243,247 | 4,422,473 | 99\.9967% | Rating<br/> \[0,5\] | √ | | √ | √ |
| 28 | EndoMondo | 1,104 | 253,020 | 253,020 | 99\.9094% | Workout Logs | √ | √ | | √ |
| 29 | Food | 226,570 | 231,637 | 1,132,367 | 99\.9978% | Rating<br/> \[0,5\] | √ | | √ | |
| 30 | GoodReads | 876,145 | 2,360,650 | 228,648,342 | 99\.9889% | Rating<br/> \[0,5\] | √ | | √ | |
| 31 | [KGRec](https://github.com/RUCAIBox/RecommenderSystems-Datasets/tree/master/dataset_info/KGRec) | - | - | - | - | Click | | | √ | |
| 32 | ModCloth | 47,958 | 1,378 | 82,790 | 99\.8747% | Rating<br/> \[0,5\] | | √ | √ | √ |
| 33 | RateBeer | 29,265 | 110,369 | 2,924,163 | 99\.9095% | Overall Rating<br/> \[0,20\] | √ | | √ | √ |
| 34 | RentTheRunway | 105,571 | 5,850 | 192,544 | 99\.9688% | Rating<br/> \[0,10\] | √ | √ | √ | √ |
| 35 | [Twitch](https://github.com/RUCAIBox/RecommenderSystems-Datasets/tree/master/dataset_info/Twitch) | 15,524,309 | 6,161,666 | 474,676,929 | 99\.9995% | Click | | | | √ |
| 36 | Amazon_M2 | 3,606,349 | 1,410,675 | 15,306,183 | \- | Click | | | √ | √ |
| 37 | Music4All-Onion | 119,140 | 109,269 | 252,984,396 | \- | Click | √ | | √ | √ |

### CTR Datasets

Expand Down
11 changes: 6 additions & 5 deletions conversion_tools/README.md
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Expand Up @@ -22,11 +22,12 @@
| 17 | Ta Feng |[Link](https://github.com/RUCAIBox/RecDatasets/blob/master/conversion_tools/usage/TaFeng.md)|
| 18 | Foursquare |[Link](https://github.com/RUCAIBox/RecDatasets/blob/master/conversion_tools/usage/Foursquare.md)|
| 19 | Tmall |[Link](https://github.com/RUCAIBox/RecDatasets/blob/master/conversion_tools/usage/Tmall.md)|
| 20 | YOOCHOOSE |[Link](https://github.com/RUCAIBox/RecDatasets/blob/master/conversion_tools/usage/YOOCHOOSE.md)|
| 21 | Retailrocket |[Link](https://github.com/RUCAIBox/RecDatasets/blob/master/conversion_tools/usage/Retailrocket.md)|
| 22 | LFM\-1b |[Link](https://github.com/RUCAIBox/RecDatasets/blob/master/conversion_tools/usage/LFM-1b.md)|
| 23 | MIND |[Link](https://github.com/RUCAIBox/RecDatasets/blob/master/conversion_tools/usage/MIND.md)|
| 24 | Music4All_Onion |[Link](https://github.com/RUCAIBox/RecSysDatasets/blob/master/conversion_tools/usage/Onion.md)|
| 20 | Tmall2014 |[Link](usage/Tmall2014.md)|
| 21 | YOOCHOOSE |[Link](https://github.com/RUCAIBox/RecDatasets/blob/master/conversion_tools/usage/YOOCHOOSE.md)|
| 22 | Retailrocket |[Link](https://github.com/RUCAIBox/RecDatasets/blob/master/conversion_tools/usage/Retailrocket.md)|
| 23 | LFM\-1b |[Link](https://github.com/RUCAIBox/RecDatasets/blob/master/conversion_tools/usage/LFM-1b.md)|
| 24 | MIND |[Link](https://github.com/RUCAIBox/RecDatasets/blob/master/conversion_tools/usage/MIND.md)|
| 25 | Music4All_Onion |[Link](https://github.com/RUCAIBox/RecSysDatasets/blob/master/conversion_tools/usage/Onion.md)|


### CTR Datasets
Expand Down
73 changes: 72 additions & 1 deletion conversion_tools/run.py
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Expand Up @@ -5,8 +5,11 @@

import argparse
import importlib
import time
from datetime import datetime

from src.utils import dataset2class, click_dataset, multiple_dataset, multiple_item_features
from src.logger import logger, format_to_str_box


if __name__ == '__main__':
Expand All @@ -29,21 +32,89 @@
assert args.input_path is not None, 'input_path can not be None, please specify the input_path'
assert args.output_path is not None, 'output_path can not be None, please specify the output_path'

# 构建配置信息
config_info = {
"数据集类型": args.dataset,
"输入路径": args.input_path,
"输出路径": args.output_path,
}

if args.interaction_type:
config_info["交互类型"] = args.interaction_type

config_info["去重模式"] = "已启用" if args.duplicate_removal else "未启用"
config_info["开始时间"] = datetime.now().strftime('%Y-%m-%d %H:%M:%S')

# 使用 logger 输出配置信息
logger.info("=" * 80)
logger.info("📊 数据集转换工具启动")
logger.info(format_to_str_box(config_info))
logger.info("=" * 80)

start_time = time.time()

input_args = [args.input_path, args.output_path]
dataset_class_name = dataset2class[args.dataset.lower()]
dataset_class = getattr(importlib.import_module('src.extended_dataset'), dataset_class_name)
if dataset_class_name in multiple_dataset:
input_args.append(args.interaction_type)
# 只有当interaction_type不为None时才添加，否则传入'all'表示处理所有行为类型
if args.interaction_type is not None:
input_args.append(args.interaction_type)
else:
input_args.append('all')
if dataset_class_name in click_dataset:
input_args.append(args.duplicate_removal)
if dataset_class_name in multiple_item_features:
input_args.append(args.item_feature_name)

logger.info(f"🔧 初始化数据集类: {dataset_class_name}")
datasets = dataset_class(*input_args)
logger.info("✅ 数据集类初始化完成")

if args.convert_inter:
logger.info("")
logger.info("=" * 80)
logger.info("🚀 开始转换交互数据 (Inter Data)")
logger.info("=" * 80)
datasets.convert_inter()
logger.info("=" * 80)
logger.info("✅ 交互数据转换完成")
logger.info("=" * 80)

if args.convert_item:
logger.info("")
logger.info("=" * 80)
logger.info("🚀 开始转换物品特征 (Item Features)")
logger.info("=" * 80)
datasets.convert_item()
logger.info("=" * 80)
logger.info("✅ 物品特征转换完成")
logger.info("=" * 80)

if args.convert_user:
logger.info("")
logger.info("=" * 80)
logger.info("🚀 开始转换用户特征 (User Features)")
logger.info("=" * 80)
datasets.convert_user()
logger.info("=" * 80)
logger.info("✅ 用户特征转换完成")
logger.info("=" * 80)

# 计算总耗时
end_time = time.time()
elapsed_time = end_time - start_time

# 构建完成信息
completion_info = {
"状态": "所有任务完成",
"总耗时": f"{elapsed_time:.2f} 秒 ({elapsed_time/60:.2f} 分钟)",
"结束时间": datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
"输出目录": args.output_path
}

logger.info("")
logger.info("=" * 80)
logger.info("🎉 转换任务执行完毕")
logger.info(format_to_str_box(completion_info))
logger.info("=" * 80)
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