The 2020 SIGIR Ecom Data Challenge
SIGIR eCom DC 2020
- URL: https://sigir-ecom.github.io/data-task.html
- Event Date: 2020-07-30 ~ 2020-07-30
- Abstract Submission Date: 2020-07-15
- Submission Date: 2020-07-22
- Location: Xi'an, China
Call for Participation
The 2020 SIGIR workshop on eCommerce is hosting the Rakuten Multi-modal Product Data Classification and Retrieval challenge. This challenge focuses on two topics, namely large-scale multi-modal (text and image) classification and cross-modal retrieval. The goal of the multi-modal classification task is to predict each product’s 'type code' as defined in the catalog of Rakuten France. In the cross-modal retrieval task, presented with the text of the products, the goal is to retrieve the images corresponding to the products.
Challenge website: https://sigir-ecom.github.io/data-task.html
Important Dates:
Data Challenge opens: April 20, 2020
Final Leaderboard - July 22, 2020
SIGIR eCom Full day Workshop - July 30, 2020
Task Description:
The cataloging of product listings through some type of text or image categorization method is a fundamental problem for any e-commerce marketplace, with applications ranging from personalized search and recommendations to query understanding. Manual and rule-based approaches to categorization are not scalable since commercial products are organized in many and sometimes thousands of classes. When actual users categorize product data, it has often been observed that not only the text of the title and description of the product is useful but also its associated images.
Tasks:
The main tasks for this challenge are as follows:
1. Multi-modal classification. Given a training set of products and their product type codes, predict the corresponding product type codes for an unseen held out test set of products. The systems are free to use the available textual titles and/or descriptions whenever available and additionally the images to allow for true multi-modal learning.
2. Cross-modal retrieval. Given an held-out test set of product items with their titles and (possibly empty) descriptions, predict the best image from among a set of test images that correspond to the products in the test set.
Participation and Data:
The data challenge is open to everyone.
For this challenge, Rakuten France has released approximately 99K product listings in tsv format, including a training (84,916) and test set (13,812). The dataset consists of product titles, product descriptions, product images and their corresponding product type codes. The test set will be released towards the end of the data challenge. Furthermore, one can assume the test set has been generated from the same data distribution as the training set.
Details about evaluation metrics and other aspects of the task can be found at the website: https://sigir-ecom.github.io/data-task.html
Important Dates:
Data Challenge opens: April 20, 2020
Final Leaderboard - July 22, 2020
SIGIR eCom Full day Workshop - July 30, 2020
The 2020 SIGIR workshop on eCommerce is hosting the Rakuten Multi-modal Product Data Classification and Retrieval challenge. This challenge focuses on two topics, namely large-scale multi-modal (text and image) classification and cross-modal retrieval. The goal of the multi-modal classification task is to predict each product’s 'type code' as defined in the catalog of Rakuten France. In the cross-modal retrieval task, presented with the text of the products, the goal is to retrieve the images corresponding to the products.
Challenge website: https://sigir-ecom.github.io/data-task.html
Important Dates:
Data Challenge opens: April 20, 2020
Final Leaderboard - July 22, 2020
SIGIR eCom Full day Workshop - July 30, 2020
Task Description:
The cataloging of product listings through some type of text or image categorization method is a fundamental problem for any e-commerce marketplace, with applications ranging from personalized search and recommendations to query understanding. Manual and rule-based approaches to categorization are not scalable since commercial products are organized in many and sometimes thousands of classes. When actual users categorize product data, it has often been observed that not only the text of the title and description of the product is useful but also its associated images.
Tasks:
The main tasks for this challenge are as follows:
1. Multi-modal classification. Given a training set of products and their product type codes, predict the corresponding product type codes for an unseen held out test set of products. The systems are free to use the available textual titles and/or descriptions whenever available and additionally the images to allow for true multi-modal learning.
2. Cross-modal retrieval. Given an held-out test set of product items with their titles and (possibly empty) descriptions, predict the best image from among a set of test images that correspond to the products in the test set.
Participation and Data:
The data challenge is open to everyone.
For this challenge, Rakuten France has released approximately 99K product listings in tsv format, including a training (84,916) and test set (13,812). The dataset consists of product titles, product descriptions, product images and their corresponding product type codes. The test set will be released towards the end of the data challenge. Furthermore, one can assume the test set has been generated from the same data distribution as the training set.
Details about evaluation metrics and other aspects of the task can be found at the website: https://sigir-ecom.github.io/data-task.html
Important Dates:
Data Challenge opens: April 20, 2020
Final Leaderboard - July 22, 2020
SIGIR eCom Full day Workshop - July 30, 2020
Journal Promotion
|
| Acta Koreana (Acta Koreana) |
|
| Acta Via Serica (AVS) |
|
| Computers and Concrete (CAC) |
|
| Journal of Information Processing Systems (JIPS) |
|
| Journal of Internet Computing and Services (KSII - JICS) |
|
| Journal of Smart Tourism (Smart Tourism) |
|
| Membrane and Water Treatment (MWT) |
|
| Wind and Structures, An International Journal (Wind and Structures) |
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