On large-scale e-commerce platforms with tens of millions of active monthly users, recommending visually similar products is essential for enabling users to efficiently discover items that align with their preferences. This study presents the application of a vision-language model (VLM) -- which has demonstrated strong performance in image recognition and image-text retrieval tasks -- to product recommendations on Mercari, a major consumer-to-consumer marketplace used by more than 20 million monthly users in Japan. Specifically, we fine-tuned SigLIP, a VLM employing a sigmoid-based contrastive loss, using one million product image-title pairs from Mercari collected over a three-month period, and developed an image encoder for generating item embeddings used in the recommendation system. Our evaluation comprised an offline analysis of historical interaction logs and an online A/B test in a production environment. In offline analysis, the model achieved a 9.1% improvement in nDCG@5 compared with the baseline. In the online A/B test, the click-through rate improved by 50% whereas the conversion rate improved by 14% compared with the existing model. These results demonstrate the effectiveness of VLM-based encoders for e-commerce product recommendations and provide practical insights into the development of visual similarity-based recommendation systems.

Visual search offers an intuitive way for customers to explore diverse product catalogs, particularly in consumer-to-consumer (C2C) marketplaces where listings are often unstructured and visually driven. This paper presents a scalable visual search system deployed in Mercari's C2C marketplace, where end-users act as buyers and sellers. We evaluate recent vision-language models for zero-shot image retrieval and compare their performance with an existing fine-tuned baseline. The system integrates real-time inference and background indexing workflows, supported by a unified embedding pipeline optimized through dimensionality reduction. Offline evaluation using user interaction logs shows that the multilingual SigLIP model outperforms other models across multiple retrieval metrics, achieving a 13.3% increase in nDCG@5 over the baseline. A one-week online A/B test in production further confirms real-world impact, with the treatment group showing substantial gains in engagement and conversion, up to a 40.9% increase in transaction rate via image search. Our findings highlight that recent zero-shot models can serve as a strong and practical baseline for production use, which enables teams to deploy effective visual search systems with minimal overhead, while retaining the flexibility to fine-tune based on future data or domain-specific needs.

Online flea market operator Mercari will enhance its confirmation process for products and expand the range of items for damage compensation, the company announced Monday. The company has been criticized on social media for not responding appropriately to sellers' complaints that different items from what they sold were returned by buyers. Mercari will set up an item collection center to compare traded merchandise with explanations from both sellers and buyers. It will also build a system equipped with artificial intelligence to detect fraud, and strengthen functions for reporting improper use of its services. "We will realize early solutions of disputes that are hard to settle between customers, by engaging better with them," a Mercari representative said.

In e-commerce platforms, coupons play a crucial role in boosting transactions. In the customer-to-customer (C2C) marketplace, ensuring the satisfaction of both buyers and sellers is essential. While buyer-focused marketing strategies often receive more attention, addressing the needs of sellers is equally important. Additionally, the existing strategies tend to optimize each promotion independently, resulting in a lack of continuity between promotions and unnecessary costs in the pursuit of short-term impact within each promotion period. We introduce a Dynamic Sequential Coupon Allocation Framework (DSCAF) to optimize item coupon allocation strategies across a series of promotions. DSCAF provides sequential recommendations for coupon configurations and timing to target items. In cases where initial suggestions do not lead to sales, it dynamically adjusts the strategy and offers subsequent solutions. It integrates two predictors for estimating the sale propensity in the current and subsequent rounds of coupon allocation, and a decision-making process to determine the coupon allocation solution. It runs iteratively until the item is sold. The goal of the framework is to maximize Return on Investment (ROI) while ensuring lift Sell-through Rate (STR) remains above a specified threshold. DSCAF aims to optimize sequential coupon efficiency with a long-term perspective rather than solely focusing on the lift achieved in each individual promotion. It has been applied for item coupon allocation in Mercari.

Mercari, Inc. is hiring for Full Time Cloud FinOps Data Analyst - Mercari - Minato City, Tokyo, Japan - Remote - a Mid-level AI, ML, Data Science role offering benefits such as Career development, Health care, Insurance, Relocation support

Understanding the dynamics of financial transactions among people is critically important for various applications such as fraud detection. One important aspect of financial transaction networks is temporality. The order and repetition of transactions can offer new insights when considered within the graph structure. Temporal motifs, defined as a set of nodes that interact with each other in a short time period, are a promising tool in this context. In this work, we study three unique temporal financial networks: transactions in Mercari, an online marketplace, payments in a synthetic network generated by J.P. Morgan Chase, and payments and friendships among Venmo users. We consider the fraud detection problem on the Mercari and J.P. Morgan Chase networks, for which the ground truth is available. We show that temporal motifs offer superior performance than a previous method that considers simple graph features. For the Venmo network, we investigate the interplay between financial and social relations on three tasks: friendship prediction, vendor identification, and analysis of temporal cycles. For friendship prediction, temporal motifs yield better results than general heuristics, such as Jaccard and Adamic-Adar measures. We are also able to identify vendors with high accuracy and observe interesting patterns in rare motifs, like temporal cycles. We believe that the analysis, datasets, and lessons from this work will be beneficial for future research on financial transaction networks.

Jumio, the leading provider of orchestrated end-to-end identity proofing, eKYC and AML solutions, announced its participation and exhibition at Money 20/20 USA, taking place October 23-26 in Las Vegas. On October 23 at 3:25 p.m. PT, Jumio will take the stage with Mercari to present "Can Online Marketplaces Survive AI-Powered Fraud?" This special one-on-one session will feature Stuart Wells, Jumio chief technology officer, and Lisa Lechner, Mercari chief compliance officer. Throughout the conference, attendees can also visit Jumio booth #2103 to learn how the global digital identity leader is accelerating digital trust by putting the power of AI and biometrics to work for account onboarding, KYC/AML compliance, identity fraud prevention, and more. Jumio will also showcase the Jumio Heat Map, based on live Jumio data and intended to simulate transactions and attack vectors as they're launched from across the globe.

Original article was published by Aniket Mishrikotkar on Deep Learning on Medium Using Machine Learning to Predict Price of Product on MercariThe numbers have no way of speaking for themselves. We …

Mercari is a marketplace app that makes it easy for people to safely sell and ship their things. Launched in 2013, the Mercari app has been downloaded over 100M times in Japan and the US. From fashion to toys, shoes to electronics and beyond, Mercari's mission is to create value in a global marketplace where anyone can buy and sell. Though we have over 1,800 employees, we still have a startup culture, where we encourage people to come up with big, crazy ideas, and to not be afraid of failure. Because the company is rapidly growing, you can set your own path, and there is enough transparency to allow our members to do so.

Mercari is a marketplace app that makes it easy for people to safely sell and ship their things. Launched in 2013, the Mercari app has been downloaded over 100M times in Japan and the US. From fashion to toys, shoes to electronics and beyond, Mercari's mission is to create value in a global marketplace where anyone can buy and sell. Though we have over 1,800 employees, we still have a startup culture, where we encourage people to come up with big, crazy ideas, and to not be afraid of failure. Because the company is rapidly growing, you can set your own path, and there is enough transparency to allow our members to do so.