A Chain-of-Thought Approach to Semantic Query Categorization in e-Commerce Taxonomies

• URL: http://arxiv.org/abs/2601.00510v1
• Date: Thu, 01 Jan 2026 23:36:13 GMT
• Title: A Chain-of-Thought Approach to Semantic Query Categorization in e-Commerce Taxonomies
• Authors: Jetlir Duraj, Ishita Khan, Kilian Merkelbach, Mehran Elyasi,
• Abstract summary: Chain-of-Thought (CoT) paradigm combines simple tree-search with semantic scoring.<n>We show how the CoT approach can detect problems within a hierarchical taxonomy.
• Score: 1.1957890510931164
• License: http://creativecommons.org/licenses/by/4.0/
• 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 scoring. Assessing its classification performance on human-judged query-category pairs, relevance tests, and LLM-based reference methods, we find that the CoT approach performs better than a benchmark that uses embedding-based query category predictions. We show how the CoT approach can detect problems within a hierarchical taxonomy. Finally, we also propose LLM-based approaches for query-categorization of the same spirit, but which scale better at the range of millions of queries.

Related papers

• Reasoning-enhanced Query Understanding through Decomposition and Interpretation [87.56450566014625]
  ReDI is a Reasoning-enhanced approach for query understanding through Decomposition and Interpretation.<n>We compiled a large-scale dataset of real-world complex queries from a major search engine.<n> Experiments on BRIGHT and BEIR demonstrate that ReDI consistently surpasses strong baselines in both sparse and dense retrieval paradigms.
  arXiv  Detail & Related papers  (2025-09-08T10:58:42Z)
• Improving E-commerce Search with Category-Aligned Retrieval [0.0]
  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.
  arXiv  Detail & Related papers  (2025-09-03T20:43:52Z)
• Chain of Retrieval: Multi-Aspect Iterative Search Expansion and Post-Order Search Aggregation for Full Paper Retrieval [68.71038700559195]
  Chain of Retrieval(COR) is a novel iterative framework for full-paper retrieval.<n>We present SCIBENCH, a benchmark providing both complete and segmented contexts of full papers for queries and candidates.
  arXiv  Detail & Related papers  (2025-07-14T08:41:53Z)
• Automated Query-Product Relevance Labeling using Large Language Models for E-commerce Search [3.392843594990172]
  Traditional approaches for annotating query-product pairs rely on human-based labeling services.<n>We show that Large Language Models (LLMs) can approach human-level accuracy on this task in a fraction of the time and cost required by human-labelers.<n>This scalable alternative to human-annotation has significant implications for information retrieval domains.
  arXiv  Detail & Related papers  (2025-02-21T22:59:36Z)
• Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity [59.57065228857247]
  Retrieval-augmented Large Language Models (LLMs) have emerged as a promising approach to enhancing response accuracy in several tasks, such as Question-Answering (QA) We propose a novel adaptive QA framework, that can dynamically select the most suitable strategy for (retrieval-augmented) LLMs based on the query complexity. We validate our model on a set of open-domain QA datasets, covering multiple query complexities, and show that ours enhances the overall efficiency and accuracy of QA systems.
  arXiv  Detail & Related papers  (2024-03-21T13:52:30Z)
• LIST: Learning to Index Spatio-Textual Data for Embedding based Spatial Keyword Queries [53.843367588870585]
  List K-kNN spatial keyword queries (TkQs) return a list of objects based on a ranking function that considers both spatial and textual relevance. There are two key challenges in building an effective and efficient index, i.e., the absence of high-quality labels and the unbalanced results. We develop a novel pseudolabel generation technique to address the two challenges.
  arXiv  Detail & Related papers  (2024-03-12T05:32:33Z)
• Hierarchical Query Classification in E-commerce Search [38.67034103433015]
  E-commerce platforms typically store and structure product information and search data in a hierarchy. Efficiently categorizing user search queries into a similar hierarchical structure is paramount in enhancing user experience on e-commerce platforms as well as news curation and academic research. The inherent complexity of hierarchical query classification is compounded by two primary challenges: (1) the pronounced class imbalance that skews towards dominant categories, and (2) the inherent brevity and ambiguity of search queries that hinder accurate classification.
  arXiv  Detail & Related papers  (2024-03-09T21:55:55Z)
• UniKGQA: Unified Retrieval and Reasoning for Solving Multi-hop Question Answering Over Knowledge Graph [89.98762327725112]
  Multi-hop Question Answering over Knowledge Graph(KGQA) aims to find the answer entities that are multiple hops away from the topic entities mentioned in a natural language question. We propose UniKGQA, a novel approach for multi-hop KGQA task, by unifying retrieval and reasoning in both model architecture and parameter learning.
  arXiv  Detail & Related papers  (2022-12-02T04:08:09Z)
• Graph Enhanced BERT for Query Understanding [55.90334539898102]
  query understanding plays a key role in exploring users' search intents and facilitating users to locate their most desired information. In recent years, pre-trained language models (PLMs) have advanced various natural language processing tasks. We propose a novel graph-enhanced pre-training framework, GE-BERT, which can leverage both query content and the query graph.
  arXiv  Detail & Related papers  (2022-04-03T16:50:30Z)
• DeepCAT: Deep Category Representation for Query Understanding in E-commerce Search [15.041444067591007]
  We propose a deep learning model, DeepCAT, which learns joint word-category representations to enhance the query understanding process. Our results show that DeepCAT reaches a 10% improvement on em minority classes and a 7.1% improvement on em tail queries over a state-of-the-art label embedding model.
  arXiv  Detail & Related papers  (2021-04-23T18:04:44Z)
• APRF-Net: Attentive Pseudo-Relevance Feedback Network for Query Categorization [12.634704014206294]
  We propose a novel deep neural model named textbfAttentive textbfPseudo textbfRelevance textbfFeedback textbfNetwork (APRF-Net) to enhance the representation of rare queries for query categorization. Our results show that the APRF-Net significantly improves query categorization by 5.9% on $F1@1$ score over the baselines, which increases to 8.2% improvement for the rare queries.
  arXiv  Detail & Related papers  (2021-04-23T02:34:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.