mercari AI


Mercari ML Engineers Chingis Oinar and Teo Narboneta Zosa will give a talk at FOSSASIA Summit2024


We are pleased to announce that Mercari ML Engineers Chingis Oinar and Teo Narboneta Zosa 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 Chingis Oinar and Teo Narboneta Zosa, engineers from the Mercari AI team, will take place as follows.

  • Title :“Unleashing the Potential of AI Through Search Ranking: How Mercari Uses Data & Science to Drive Marketplace Growth”
  • The presentation Time : Tuesday, April 9, 2024, 11:00 - 11:35 GMT+7
  • Detail :

Last time Mercari ML Engineers visited FOSSASIA Summit 2023 in Singapore to share their experience of implementing Machine Learning Operations (MLOps) within Mercari, diving into details of the associated integration challenges as well as how they managed to develop solutions that could be efficiently scaled and modified.

This time they are back to present the inner workings of Mercari's ranking system, delving more into details of ML solutions that drive the marketplace growth:


In today's competitive e-commerce landscape, effective search ranking systems are no longer a luxury, but a necessity. At Mercari, Japan’s largest C2C e-commerce marketplace, we are leveraging and continuously integrating AI into search to provide the best experience for our millions of users, driving engagement to optimize marketplace transactions and the joy of discovery.

This talk is full of insights from our ongoing journey building and refining Mercari’s search ranking system, geared towards data scientists, machine learning engineers, and anyone interested in developing effective unbiased real-time production AI solutions for e-commerce search ranking.

Key points of presentation

The presentation by Chingis and Teo at the FOSSASIA Summit 2024 is an in-depth look into the company’s machine learning ranking system. They will cover four main points needed to develop an efficient ML solution: dataset construction, model tracking and monitoring, model building, and continuous iteration.

Summary of paper

They will cover four main points needed to develop an efficient ML solution: dataset construction, model tracking and monitoring, model building, and continuous iteration.

  • Dataset Construction. Problem formulation and understanding the problem at hand forms half of the solution. In the ever-evolving e-commerce search scene, it’s important to constantly innovate, adapt and refine the training data used. Chingis and Teo will be demonstrating how we build a rich and diverse dataset incorporating user behavior, item attributes, and marketplace dynamics.
  • Model Tracking & Monitoring. Constant innovation and iteration means recognizing the progress. Therefore, they will aso share how we approach tracking and monitoring our ML ranking models, including key custom metrics we’ve developed for robust evaluation.
  • Model Building. After formulating the problem and setting up a process to monitor the learning progression, they will delve into the technical details of our model building process, exploring what learning-to-rank is as well as our goals for optimal performance.
  • De-biasing through Historical Data. Finally, acknowledging existing problems is the first step towards continuous iteration and growth. Chingis and Teo will outline the potential problems inherent in implicit feedback and how we deal with potential biases in our ranking models through counterfactual learning. They will show how biased behavior affects the learning process and what factors can trigger specific user behaviors.

About the 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.


ML Engineer

Chingis Oinar

  • Data Science
  • Multimodal
  • Search

Chingis is a Data Scientist and an Engineer at Mercari working on search relevancy projects and solving problems in various domains, including Learning-to-Rank and NLP. He is also a researcher and technical writer with an interest in self-supervised, multimodal, and representation learning.

ML Engineer

Teofilo Narboneta Zosa

  • Deep Learning
  • MLOps
  • Search

Teo is an accomplished ML Engineer currently working within Mercari’s AI & Search division. His primary focus is on architecting and implementing highly scalable and resilient ML systems to significantly enhance search relevancy for Mercari’s over 20 million monthly active users. In addition, Teo plays a crucial role in spearheading the establishment and continuous improvement of foundational cross-team MLOps platforms, workflows, and engineering guidelines across Mercari.

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