Shopping Queries Image Dataset (SQID): An Image-Enriched ESCI Dataset for Exploring Multimodal Learning in Product Search

TL;DR

This paper introduces SQID, a large multimodal dataset combining shopping queries with product images, enabling research on multimodal learning to improve product search and ranking in online shopping.

Contribution

The paper presents SQID, a new dataset integrating images with shopping queries, facilitating multimodal learning research for enhanced search and ranking performance.

Findings

Multimodal data improves search relevance.

Pretrained models benefit from visual information.

SQID enables new research in multimodal product search.

Abstract

Recent advances in the fields of Information Retrieval and Machine Learning have focused on improving the performance of search engines to enhance the user experience, especially in the world of online shopping. The focus has thus been on leveraging cutting-edge learning techniques and relying on large enriched datasets. This paper introduces the Shopping Queries Image Dataset (SQID), an extension of the Amazon Shopping Queries Dataset enriched with image information associated with 190,000 products. By integrating visual information, SQID facilitates research around multimodal learning techniques that can take into account both textual and visual information for improving product search and ranking. We also provide experimental results leveraging SQID and pretrained models, showing the value of using multimodal data for search and ranking. SQID is available at: https://github.com/Crossing-Minds/shopping-queries-image-dataset.