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This research addresses the critical challenge of linking short, unstructured e-commerce search queries to specific brand entities using advanced machine learning techniques. The authors propose scalable solutions ranging from cascaded sequence labeling pipelines to single-stage extreme multiclass classification models, demonstrating significant improvements in recall and precision across 11 languages. These methods directly enhance product retrieval accuracy, which is a core application of natural language processing within large-scale commercial systems.
Query Brand Entity Linking in E-Commerce Search
arXiv:2502.01555v3 Announce Type: replace-cross Abstract: Associating user search queries with the correct brand entity is critical for e-commerce product retrieval, yet remains challenging due to the brevity of queries (three to four words on average), their lack of grammatical structure, and a catalog of hundreds of thousands of distinct brands. We formulate this as a brand entity linking task and develop two complementary solutions deployed at scale: (1) a cascaded pipeline that first detects brand mentions via sequence labeling and then disambiguates against a brand knowledge base, and (2) a single-stage approach that frames linking as extreme multiclass classification, directly mapping queries to brand identifiers. Through extensive multilingual evaluation (11 languages) and a controlled online experiment, we demonstrate that the proposed methods substantially improve brand recall while maintaining high precision, leading to measurable gains in customer engagement.
Abstract:Associating user search queries with the correct brand entity is critical for e-commerce product retrieval, yet remains challenging due to the brevity of queries (three to four words on average), their lack of grammatical structure, and a catalog of hundreds of thousands of distinct brands. We formulate this as a brand entity linking task and develop two complementary solutions deployed at scale: (1) a cascaded pipeline that first detects brand mentions via sequence labeling and then disambiguates against a brand knowledge base, and (2) a single-stage approach that frames linking as extreme multiclass classification, directly mapping queries to brand identifiers. Through extensive multilingual evaluation (11 languages) and a controlled online experiment, we demonstrate that the proposed methods substantially improve brand recall while maintaining high precision, leading to measurable gains in customer engagement.
Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
arXiv:2502.01555 [cs.IR]
(or arXiv:2502.01555v3 [cs.IR] for this version)
https://doi.org/10.48550/arXiv.2502.01555 Focus to learn more arXiv-issued DOI via DataCite
https://doi.org/10.1145/3799682.3840092 https://doi.org/10.1145/3799682.3840092 Focus to learn more DOI(s) linking to related resources
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