README.md

---
license: cc-by-nc-sa-4.0
tags:
- recsys
- e-commerce
- retrieval
- dataset
- ranking
- cross-domain
language:
- ru
- en
size_categories:
- 100B<n<1T
pretty_name: T-ECD
---


# T-ECD: T-Tech E-commerce Cross-Domain Dataset

![image (2)](https://cdn-uploads.huggingface.co/production/uploads/645d4947f5760d1530d55023/t8w2QTQbnH0DhEIbJYCnT.png)

⭐️ **T-ECD** is a large-scale synthetic cross-domain dataset for recommender systems research, created by T-Bank's RecSys R&D team. 
It captures real-world e-commerce interaction patterns across multiple domains while ensuring complete anonymity through synthetic generation.

🎯 Overview
T-ECD represents user interactions across five different e-commerce domains within a banking ecosystem:

- **Marketplace** — browsing and interacting with items in an e-commerce marketplace.  
- **Retail** — interactions within a retail delivery service, including cart additions and completed orders.  
- **Payments** — online and offline financial transactions between users and brands.  
- **Offers** — responses to promotional content such as impressions, clicks, and partner transitions.  
- **Reviews** — explicit user feedback in the form of ratings and embeddings of textual comments.  

**Scale:**  
- **~135B** interactions  
- ~44M users  
- ~30M items  
- **1300+ days of temporal coverage**  


Additionally, we provide **T-ECD Small** - a compact version containing 1B interactions that excludes the Payments domain.

<div style="font-size: 1.1em;">

| Metric | T-ECD Small | T-ECD Full |
|--------|-------------|------------|
| 🔄 **Interactions** | ~1B | **~135B** |
| 👥 **Users** | ~3.5M | **~44M** |
| 📦 **Items** | ~2.6M | **~30M** |
| 🏪 **Brands** | ~29K | **~1M** |
| 📅 **Temporal Coverage** | 200+ days | **1300+ days** |
| 🌐 **Domains** | 4 (excl. Payments) | **5 (all domains)** |

</div>


<img src="https://cdn-uploads.huggingface.co/production/uploads/645d4947f5760d1530d55023/Y3hHv_cipdq2p4A9jiQoz.png" style="max-width: 80%; height: auto;">


Cross-domain consistency is achieved by aligning identifiers across all domains:  
- the same `user_id` always refers to the same individual user, and  
- the same `brand_id` always refers to the same brand entity.  

This alignment allows researchers to seamlessly link interactions from different services, enabling studies in transfer learning, cross-domain personalization, and multi-task modeling.

<img src="https://cdn-uploads.huggingface.co/production/uploads/645d4947f5760d1530d55023/QG0DavvcvccN1GcN_gRL6.png" style="max-width: 80%; height: auto;">
<img src="https://cdn-uploads.huggingface.co/production/uploads/645d4947f5760d1530d55023/s8a8iC4RmUjsD_hzOVPvD.png" style="max-width: 80%; height: auto;">

---

### 📂 Data Schema

The dataset is stored in **Parquet** format with daily partitions (`{day}`).  
The directory structure is as follows:

```
t-ecd/
├── users.pq
├── brands.pq
├── marketplace/
│ ├── events/{day}.pq
│ └── items.pq
├── retail/
│ ├── events/{day}.pq
│ └── items.pq
├── payments/
│ ├── events/{day}.pq
│ └── receipts/{day}.pq
├── offers/
│ ├── events/{day}.pq
│ └── items.pq
└── reviews/{day}.pq
```

#### Data availability
<img src="https://cdn-uploads.huggingface.co/production/uploads/645d4947f5760d1530d55023/c2Clc9bNxL9i7jgGBfBq2.png" style="max-width: 80%; height: auto;" alt="Temporal distribution of events over domains">
*Temporal distribution of events over domains*
In line with real-world industrial environments, domain-specific data availability varies in historical depth. 
This reflects practical constraints including data retention policies and product lifecycle stages - 
newer e-commerce services naturally have shorter histories compared to established banking domains like payments and transactions.


### ⚙️ Events and Catalogs

- **Events**: Each domain provides logs of user interactions with the following possible columns:

  - `action_type` — interaction type (e.g., view, click, add-to-cart, order, transaction).  
  - `subdomain` — surface where the interaction occurred (recommendations, catalog, search, checkout, campaign); available in Marketplace and Retail.  
  - `item_id` — present in Marketplace, Retail, and Offers; identifies a specific product or offer.  
  - `brand_id` — present in all domains; denotes the seller, store, or partner associated with an item, offer, or transaction.  
  - `price` —  represents the monetary value of the interaction.
  - `count` — represents the amount of items in single interaction.
  - `os` — user operating system, available in Marketplace and Retail.

<img src="https://cdn-uploads.huggingface.co/production/uploads/645d4947f5760d1530d55023/Q7aeb_I-Yf-rcqyPDTOLa.png" style="max-width: 80%; height: auto;" >

- **Item catalogs (`items.pq`)**: Available for Marketplace, Retail, and Offers. Each entry includes:  
  - `item_id`  
  - `brand_id`  
  - category information (if available)  
  - pretrained embedding (if available)  

- **User catalog (`users.pq`)**: Contains anonymized user attributes such as region and socio-demographic cluster.  

- **Brand catalog (`brands.pq`)**: Contains `brand_id`, brand-level metadata, and embeddings.  

#### 🧾 Special Structures

- **Receipts (`payments/receipts/{day}.pq`)**:  
  Some transactions include detailed receipts with purchased items, their quantities, and prices.  
  Items are aligned with Marketplace and Retail catalogs, enabling fine-grained cross-domain linkage at the product level.  

- **Reviews (`reviews/{day}.pq`)**:  
  Provide explicit ratings per brand.  
  Raw text reviews are not included; instead, we release pretrained text embeddings to preserve privacy while enabling multimodal research.  
---

### 🛠️ Data Collection

T-ECD was generated through a multi-step process:  
1. **Sampling of event chains**: sequences of interactions were sampled from real logs of T-Bank ecosystem services.  
2. **Anonymization**: user and brand identifiers were pseudonymized; sensitive attributes removed.  
3. **Synthetic generation**: based on real distributions and event patterns, new synthetic interaction chains were produced, preserving structural properties such as sparsity, heavy tails, cross-domain overlaps, and behavioral contexts.  

This process ensures that the dataset is privacy-preserving while remaining representative of industrial recommender system data.  

## ⚠️ Important Note on Temporal Data Usage


<img src="https://cdn-uploads.huggingface.co/production/uploads/645d4947f5760d1530d55023/zaPAcuD3CItTzP2PBkErs.png" style="max-width: 80%; height: auto;">


**To prevent data leakage, events from the final 12 hours should not be used for prediction tasks.** 
The dataset contains temporal noise that requires maintaining a minimum 12-hour gap between the timestamp of the most recent user event and the prediction timestamp.
This constraint applies to both training and testing scenarios to avoid temporal data leakage.


## Download

#### Basic Download

```python
from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="t-tech/T-ECD",
    repo_type="dataset",
    allow_patterns="dataset/full/",
    local_dir="./t_ecd_data",
    token="<your_hf_token>" 
)
```


#### Selective Download

For advanced usage including selection of domains and date ranges we provide custom downloader [tecd_downloader.py](https://huggingface.co/datasets/t-tech/T-ECD/blob/main/tecd_downloader.py)

Example usage:

```python
from tecd_downloader import download_dataset

download_dataset(
    token="<your_hf_token>",
    dataset_path="dataset/small",
    local_dir="t_ecd_small_partial",
    domains=["retail", "marketplace"],
    day_begin=1300,
    day_end=1308,
    max_workers=10
)
```


---

### 🔐 License

This dataset is released under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0) licence


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