Update README.md

README.md

@@ -1,13 +1,137 @@
 ---
 license: apache-2.0
-language:
-- en
-- ru
 tags:
-- finance
-- e-commerce
 - recsys
-pretty_name: T-ECD
+- e-commerce
+- retrieval
+- dataset
+- ranking
+- cross-domain
+language:
+- ru
+- en
 size_categories:
-- n>1T
+- 100B<n<1T
+pretty_name: T-ECD
+---
+
+
+
+# T-ECD: T-Tech E-commerce Cross-Domain Dataset
+
+![Habr_cover_cat@1x](https://cdn-uploads.huggingface.co/production/uploads/645d4947f5760d1530d55023/WTyxU8ugrf-XzHBjpoGq-.jpeg)
+
+⭐️ **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**  
+
+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.
+
+📊 *[Graph 1: Distribution of interactions per user (heavy tail)]*  
+📊 *[Graph 2: Overlap of users across domains]*  
+
+---
+
+### 📂 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
+```
+
+### ⚙️ Events and Catalogs
+
+- **Events**: Each domain provides logs of user interactions with the following possible columns:
+  
+  In events you can encounter such 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.
+
+
+- **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)  
+
+📊 *[Graph 3: Distribution of event types per domain]*  
+📊 *[Graph 4: Distribution of subdomains (e.g., recommendations vs catalog)]*  
+
+- **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.  
+
+📊 *[Graph 5: Temporal coverage and dataset scale]*  
+
+---
+
+### 🔐 License
+
+This dataset is released under the Apache License 2.0.
+
 ---