Wright, L.; Carter, A.; Nasir, W. Enhancing Commercial Interactions through Comprehensive QA Fusion. Preprints2024, 2024040493. https://doi.org/10.20944/preprints202404.0493.v1
APA Style
Wright, L., Carter, A., & Nasir, W. (2024). Enhancing Commercial Interactions through Comprehensive QA Fusion. Preprints. https://doi.org/10.20944/preprints202404.0493.v1
Chicago/Turabian Style
Wright, L., Aiden Carter and Wyne Nasir. 2024 "Enhancing Commercial Interactions through Comprehensive QA Fusion" Preprints. https://doi.org/10.20944/preprints202404.0493.v1
Abstract
In the dynamic landscape of E-commerce, the proliferation of user-generated queries regarding products highlights the critical need for advanced automatic question answering (AQA) systems. These systems are instrumental in harnessing the vast array of information available online to deliver immediate and informative responses to potential buyers. Recognizing the complexity of such queries, which often require synthesis of reviews, product specifications, and responses to similar questions, we introduce a sophisticated model, the Comprehensive E-commerce Answer Generation System (CEAGS). This model adeptly navigates the challenges posed by irrelevant data and sentiment ambiguity in user-generated content. Our approach leverages a dual-phase process of relevance determination and sentiment clarity, setting the stage for a transformative response generation mechanism. Empirical analyses reveal the superiority of CEAGS, with our relevance assessment framework outstripping existing models by a notable margin in precision metrics, and our answer generation module achieving unprecedented gains in content preservation and coherence. Notably, CEAGS marks a pioneering contribution to the E-commerce domain by integrating disparate information sources to formulate responses that are not only accurate but also contextually rich and user-centric.
Keywords
natural language processing; transformer; question answering
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
