Automated Catalog Deduplication

One of the most extensively documented implementations involves a major e-commerce marketplace in Turkey operating a catalog of more than 300 million products, with over 300,000 new items uploaded daily and 1.2 million product updates flowing through its systems each day. As described in a Dec. 2025 engineering disclosure, the marketplace built a fully event-driven, modular backend using vector databases and AI models trained on both text and image data to perform deduplication in near real-time. The engineering team reported that traditional keyword-based and rule-based matching methods failed at this scale because the systems could not capture semantic meaning across variant product descriptions. The resulting AI-powered system processes and matches millions of products in near real-time, using separate deployment pipelines for data ingestion and search to maintain stability under heavy concurrent load.

A peer-reviewed study published in Expert Systems with Applications in 2022 documented the development of a duplicate record detection engine for the same marketplace. The research team built a training set of 34,007 product pairs, of which 12,324 were confirmed duplicates, using text similarity and domain-specific distance metrics with human expert labeling. The study confirmed that supervised machine learning models trained on labeled duplicate and non-duplicate pairs can detect duplicate product records with high precision, particularly when domain-specific preprocessing such as brand name normalization and format standardization is applied. In a separate case, a brand management agency identified 108 duplicate listings for a single client on a major U.S. marketplace, representing an opportunity to consolidate fragmented search results and recover $93,822 in sales attributed to those duplicate pages.