Towards Better Search with Domain-Aware Text Embeddings for C2C Marketplaces

• URL: http://arxiv.org/abs/2512.21021v1
• Date: Wed, 24 Dec 2025 07:35:17 GMT
• Title: Towards Better Search with Domain-Aware Text Embeddings for C2C Marketplaces
• Authors: Andre Rusli, Miao Cao, Shoma Ishimoto, Sho Akiyama, Max Frenzel,
• Abstract summary: We build a domain-aware Japanese text-embedding approach to improve the quality of search at Mercari, Japan's largest C2C marketplace.<n>To meet production constraints, we apply Matryoshka Representation Learning to obtain compact, truncation-robust embeddings.
• Score: 3.8273208793317743
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Consumer-to-consumer (C2C) marketplaces pose distinct retrieval challenges: short, ambiguous queries; noisy, user-generated listings; and strict production constraints. This paper reports our experiment to build a domain-aware Japanese text-embedding approach to improve the quality of search at Mercari, Japan's largest C2C marketplace. We experimented with fine-tuning on purchase-driven query-title pairs, using role-specific prefixes to model query-item asymmetry. To meet production constraints, we apply Matryoshka Representation Learning to obtain compact, truncation-robust embeddings. Offline evaluation on historical search logs shows consistent gains over a strong generic encoder, with particularly large improvements when replacing PCA compression with Matryoshka truncation. A manual assessment further highlights better handling of proper nouns, marketplace-specific semantics, and term-importance alignment. Additionally, an initial online A/B test demonstrates statistically significant improvements in revenue per user and search-flow efficiency, with transaction frequency maintained. Results show that domain-aware embeddings improve relevance and efficiency at scale and form a practical foundation for richer LLM-era search experiences.

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