A Chain-of-Thought Approach to Semantic Query Categorization in e-Commerce Taxonomies
Jetlir Duraj, Ishita Khan, Kilian Merkelbach, Mehran Elyasi

TL;DR
This paper introduces a Chain-of-Thought approach combining tree-search and LLM semantic scoring for accurate query categorization in e-Commerce taxonomies, improving relevance and taxonomy quality detection.
Contribution
It presents a novel Chain-of-Thought paradigm for semantic query categorization that outperforms embedding-based benchmarks and scales for large query volumes.
Findings
CoT approach outperforms embedding-based benchmarks
Detects problems within hierarchical taxonomies
Scales to millions of queries with LLM-based methods
Abstract
Search in e-Commerce is powered at the core by a structured representation of the inventory, often formulated as a category taxonomy. An important capability in e-Commerce with hierarchical taxonomies is to select a set of relevant leaf categories that are semantically aligned with a given user query. In this scope, we address a fundamental problem of search query categorization in real-world e-Commerce taxonomies. A correct categorization of a query not only provides a way to zoom into the correct inventory space, but opens the door to multiple intent understanding capabilities for a query. A practical and accurate solution to this problem has many applications in e-commerce, including constraining retrieved items and improving the relevance of the search results. For this task, we explore a novel Chain-of-Thought (CoT) paradigm that combines simple tree-search with LLM semantic…
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Taxonomy
TopicsInformation Retrieval and Search Behavior · Text and Document Classification Technologies · Data Quality and Management
