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Institutional Repository [SANDBOX]
Technical University of Crete
Technical University of Crete
EN
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| URI: | http://purl.tuc.gr/dl/dias/3872B280-EDE9-4ACF-80FF-901FEEEED9C9 | ||
| Year | 2024 | ||
| Type of Item | Diploma Work | ||
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| Bibliographic Citation | Konstantinos Koulaouzidis, "MEREBO2: A web mart analyzer for multi-source entity resolution and basket optimization over Android mobile devices", Diploma Work, School of Electrical and Computer Engineering, Technical University of Crete, Chania, Greece, 2024 https://doi.org/10.26233/heallink.tuc.98511 | ||
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In today’s fast-paced world of e-commerce, where an ever-expanding array of products is available to the customers, the ability to efficiently and accurately identify similar products has become a critical component of online market analysis. In this context, image comparison techniques have emerged as a powerful tool for product similarity analysis. This thesis embarks on a comprehensive exploration of image comparison algorithms and methods specifically tailored for the identification of similar products to enhance the performance and credibility of MEREBO. Furthermore, while the identification of similar products through image comparison is undeniably valuable, the scalability and execution time of such processes remain significant challenges. In the era of online retail, where vast product catalogs are common, it is imperative to develop algorithms and systems to not only provide accurate results but also do so in a swift and resource-efficient manner. Hence, the second focal point of this thesis is to address the pressing issue of execution time with the ultimate goal to make it appealing on real world applications of e-commerce. The combination of product similarity accuracy and time efficiency will contribute to a more seamless and productive online shopping experience, benefiting both the consumers and retailers alike. The last pivotal aspect of this thesis centers around the practical applicability of such systems in the mobile environment. This entails addressing the unique challenges posed by mobile platforms, such as limited computational resources, varying screen sizes and diverse network conditions. This third dimension, alongside accuracy and time efficiency, forms a holistic approach to advancing product similarity analysis in the context of e-commerce, aiming to improve the way consumers interact with and make informed choices in the digital marketplace.
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