Extracting Important Tokens in E-Commerce Queries with a Tag Interaction-Aware Transformer Model
Md. Ahsanul Kabir, Mohammad Al Hasan, Aritra Mandal, Liyang Hao, Ishita Khan, Daniel Tunkelang, Zhe Wu

TL;DR
This paper introduces TagBERT, a transformer-based model that leverages semantic tags for token classification to improve query reformulation in e-commerce search, outperforming existing models.
Contribution
The paper proposes a novel dependency-aware transformer model, TagBERT, which utilizes semantic token tags to enhance query reformulation accuracy in e-commerce search.
Findings
TagBERT outperforms BERT, eBERT, and sequence-to-sequence models.
Semantic tags significantly improve token classification.
Model demonstrates superior performance on large real-world datasets.
Abstract
The major task of any e-commerce search engine is to retrieve the most relevant inventory items, which best match the user intent reflected in a query. This task is non-trivial due to many reasons, including ambiguous queries, misaligned vocabulary between buyers, and sellers, over- or under-constrained queries by the presence of too many or too few tokens. To address these challenges, query reformulation is used, which modifies a user query through token dropping, replacement or expansion, with the objective to bridge semantic gap between query tokens and users' search intent. Early methods of query reformulation mostly used statistical measures derived from token co-occurrence frequencies from selective user sessions having clicks or purchases. In recent years, supervised deep learning approaches, specifically transformer-based neural language models, or sequence-to-sequence models…
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Taxonomy
TopicsWeb Data Mining and Analysis · Service-Oriented Architecture and Web Services · Spam and Phishing Detection
