Çevrimiçi pazaryerleri için derin sinir ağlarına dayalı ürün üstbilgisi geçerleme yaklaşımı
Year: 2023
Type: dissertation
Abstract: As e-commerce has become increasingly popular during the pandemic, online marketplaces have seen a surge in merchants offering various products. A critical factor in the success of these marketplaces is the user experience they provide, largely dependent on features like efficient product search, fast filtering, and attractive product images. However, maintaining the data quality of product metadata and images can be challenging, especially as the number of products grows exponentially. To address this issue, this research proposes a novel approach using an AI-based automated image validation model for validating product images and an AI-based classification model to validate product metadata in an automated fashion. Our approach offers several advantages over traditional methods, including handling complex and noisy data and adapting to various challenging product categories, such as fashion items. We demonstrate the effectiveness of this approach through comparisons with traditional methods and in different settings, ultimately showing strong support for the use of AI in product metadata validation for online marketplaces.
Author: Alataş, Şükrü
Language: English
Topic: Data Quality and Management
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7112618817