Mercari ML Engineers Lirong Zhang and Miao Cao will give a talk at FOSSASIA Summit2024

Overview

We are pleased to announce that Mercari ML Engineers Lirong Zhang and Miao Cao will have a presentation at FOSSASIA Summit 2024, held from Monday 8th April to Wednesday 10 April.

FOSSASIA is an Asia-based organization that develops open source software applications and open hardware with the global community. The event aims to create connections between developers, vendors and users. With a strong emphasis on OSS, the topics will range from Hardware/AI/Cloud technologies/Databases/Mobile/IT Education and Legal Compliance...etc.

Presentations by Lirong Zhang and Miao Cao, engineers from the Mercari AI team, will take place as follows.

• Title : “Opportunities to Address Complex Challenges in E-Commerce Search Through LLMs”
• The presentation Time : Tuesday, April 9, 2024, 14:45 - 15:10 GMT+7
• Detail：https://eventyay.com/e/55d2a466/session/9015

Background

E-commerce search mechanisms often grapple with multifaceted problems, such as the scarcity and subjectivity of human-annotated datasets and the absence of comprehensive synonym dictionaries, among others. In tackling these challenges, Mercari search team has strategically embraced Large Language Model (LLM)-based approaches across various application scenarios in the search domain.

Summary of paper

This talk aims to disseminate valuable insights from our ongoing exploration into LLM-based features within e-commerce search. It is tailored for data scientists, machine learning engineers, and anyone fascinated by the prospects of deploying effective LLM solutions in e-commerce search contexts. Join us as we delve into our journey, highlighting our strategies, successes, and the lessons learned along the way in harnessing the power of LLMs to resolve long-standing and intricate problems in e-commerce search.

Key points of presentation

In this presentation, we will share our recent endeavors to leverage LLMs to augment our search system capabilities significantly. This presentation outlines our innovative methods, including:

• Offline Evaluation Dataset Construction: We will demonstrate the creation of an offline evaluation system designed to significantly boost development productivity. This system allows for a more streamlined and efficient process of assessing the performance of our search algorithms, ensuring constant improvement and optimization.

• Synonym Generation: Delving into the mechanisms through which LLMs can enhance our synonym dictionary, making it more comprehensive. This is crucial for understanding and correlating user queries with the vast array of products listed on our platform, particularly when dealing with varying linguistic expressions and regional vernaculars.

• Image Quality Assessment: Illustrating the application of LLMs in evaluating and categorizing images for image quality model development. This approach aids in improving the quality of search results, providing a more satisfying user experience.

About Search Quality Team

The Search Quality Team is committed to enhancing the quality of search results. We focus on continuous improvement on search ranking and relevance to provide our users with a satisfying shopping and selling journey.