Improving E-commerce Search with Category-Aligned Retrieval

• URL: http://arxiv.org/abs/2510.21711v1
• Date: Wed, 03 Sep 2025 20:43:52 GMT
• Title: Improving E-commerce Search with Category-Aligned Retrieval
• Authors: Rauf Aliev,
• Abstract summary: Category-Aligned Retrieval System (CARS) improves search relevance by first predicting the product category from a user's query and then boosting products within that category.<n>We introduce a novel method for creating "Trainable Category Prototypes" from query embeddings.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Traditional e-commerce search systems often struggle with the semantic gap between user queries and product catalogs. In this paper, we propose a Category-Aligned Retrieval System (CARS) that improves search relevance by first predicting the product category from a user's query and then boosting products within that category. We introduce a novel method for creating "Trainable Category Prototypes" from query embeddings. We evaluate this method with two models: a lightweight all-MiniLM-L6-v2 and OpenAI's text-embedding-ada-002. Our offline evaluation shows this method is highly effective, with the OpenAI model increasing Top-3 category prediction accuracy from a zero-shot baseline of 43.8% to 83.2% after training. The end-to-end simulation, however, highlights the limitations of blindly applying category boosts in a complex retrieval pipeline: while accuracy is high, naive integration can negatively affect search relevance metrics such as nDCG@10. We argue that this is partly due to dataset-specific ambiguities (e.g., polysemous queries in the Amazon ESCI corpus) and partly due to the sensitivity of retrieval systems to over-constraining filters. Crucially, these results do not diminish the value of the approach; rather, they emphasize the need for confidence-aware and adaptive integration strategies.

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