mercari AI


Mercari DS Teams’s research “Using Recommendations to balance demand and supply in two-sided marketplaces” accepted to EcoSys Workshop at AAAI 2024


We are pleased to announce that "Using Recommendations to balance demand and supply in two-sided marketplaces", an extended abstract authored by Data Science Team engineer Deddy Jobson, has been accepted as a poster to the Recommendation Ecosystems Workshop (EcoSys2024) of AAAI 2024 (The Conference on Information and Knowledge Management) , an international conference on artificial intelligence.

AAAI is one of the most prestigious international conferences in the field of artificial intelligence technologies, held annually by researchers from around the world. This year's AAAI, in its 38th year, will be held in Vancouver, Canada, February 20-27, 2024.

The following schedule of poster presentation times is planned

  • Monday, February 26, 2024, 3:00 p.m. - 4:00 p.m. (local time)

■Key points of presentation

*You can read the entire paper at this link.

  • We propose the idea of using recommendation systems to balance demand and supply in two-sided marketplaces consisting of buyers and sellers.
  • We perform experiments on a public dataset to show how the recommendations made by any recommender system can be modified to improve buyer-side metrics or seller-side metrics depending on the business priorities.


Current recommender systems are consumer-centric in that their objective is to improve the benefit to consumers. This helps with dealing with the excessive supply of content for consumers.

We introduce the idea of item-centric recommendations that help tackle the complementary problem that can arise in a two-sided marketplace: excess demand. We show using experiments on public datasets that recommendations can be used to prioritize seller experience or buyer experience depending on the business requirements.

■Summary of paper

  • We use recommender systems to help balance demand and supply in two-sided marketplaces.
  • We do so by constructing a lever that one can use to shift the nature of the recommendations towards helping producers or consumers accordingly based on the business objective.
  • We believe our approach can extend the capabilities of recommender systems to improve the long-term welfare of all participants of the two-sided marketplace.

■About Data Science Team

The Data Science Team makes use of statistics, machine learning, and mathematical optimization to improve the efficiency of Mercari’s marketing campaigns and core product. We actively learn and adopt the state of the art methods used in the literature to solve technical problems and publish papers along the way.


ML Engineer

Deddy Jobson

  • Causal Inference
  • Marketing Strategy
  • Statistical Machine Learning

Deddy is a ML Engineer who joined Mercari as a new graduate. He is responsible for analyzing marketing campaigns using statistical models and mathematical optimization. On top of predicting which users respond positively or negatively to campaigns, Deddy also provides detailed explanations for those behaviors. He then shares those insights with stakeholders to discuss ways to improve future campaigns.

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