Ηλεκτρονικό εμπόριο με γνώμονα το λογισμικό: Βελτιστοποίηση Ευχρηστίας, Εμπειρίας Χρήστη, Προσβασιμότητας και Επισκεψιμότητας βάσει Μηχανικής Μάθησης, Επεξεργασίας Φυσικής Γλώσσας, Μεγάλων Γλωσσικών Μοντέλων και τεχνικών Βελτιστοποίησης Μηχανών Αναζήτησης

Abstract

This thesis examines the techniques and technologies that can lead to a more optimized, more accessible, and more sustainable WEB and E-commerce. The development of software tools in both PHP and Python programming languages is undertaken, leveraging advanced Large Language Models (LLMs) and Natural Language Processing (NLP) to automate E-commerce processes previously deemed inaccessible. To confirm and enhance the results of the research, data analysis tools, predictive modeling, and Machine Learning (ML) algorithms were utilized. Throughout this research, all the aforementioned technologies are harmoniously combined, leading to an E-commerce that will not only survive but thrive in the future's intense competition. This research is divided into four distinct chapters, each specialized in one of the aforementioned technologies. The investigation begins with LLMs, exploring ways to integrate them into E-commerce and how their advanced Artificial Intelligence (ΑΙ) and NLP capabilities can automate E-commerce processes. Subsequently, an exploration of Web Accessibility is undertaken, highlighting its status as an emerging domain for future consideration within the virtual landscape. This occurs notwithstanding the escalating demand for an enhanced level of accessibility on the web. Progressive Web Apps (PWA), a new web technology relying on cutting-edge technologies to transform web pages, particularly E-commerce, into a multi-device tool, increasing accessibility and usability, are then explored. Subsequently, an evaluation of PWAs regarding accessibility and their ability to deliver on promises is conducted. Subsequent to the ongoing research, each aspect of Search Engine Optimization (SEO) is systematically examined, exploring opportunities within diverse domains under E-commerce umbrella, including retail E-commerce and service-oriented sectors such as booking platforms. This involves identifying SEO techniques and technologies that exhibit heightened efficacy in securing elevated rankings on search engines and fostering increased organic traffic. Another technology, Accelerated Mobile Pages (AMP), and how it contributes to the growth of an E-commerce's visibility is finally explored. During the exploration of LLMs, the initial focus was on investigating NLP and its capacity for understanding human language, the Generative Pre-trained Transformer (GPT) architecture, and its innovative use of transformers and self-attention mechanisms to process input sequences. This encompassed the pre-training phase involving billion parameters utilized in the training of GPT models, as well as their capability to undergo fine-tuning for domain-specific tasks. Subsequently, specific attention was given to GPT-3.5, GPT-4, and LLaMA-2 models. Their integration into Ecommerce was scrutinized, and potential enhancements, such as automation, were explored to significantly improve both functional and customer-centered aspects of online commerce. It's important to note that this exploration went beyond LLMs, delving into renowned NLP models such as BERT and RoBERTa. Additionally, unsupervised and supervised learning algorithms like k-means clustering, content-based filtering (CBF), hierarchical clustering, as well as logistic regression and neural network algorithms were examined. To achieve the research objectives, Chrome Apps and flask-based APIs were developed using Python and JavaScript. The aforementioned models underwent fine-tuning through few-shot learning tailored for specific domains, providing valuable insights into the integration of LLMs and NLP within the realm of E-commerce. The focus extended to hot topics, including sentiment analysis, recommender systems, sustainable purchasing decisions, and churn modeling. This comprehensive examination aimed to uncover the practical applications and benefits of leveraging advanced language models for optimizing the E-commerce landscape. In the examination of Web Accessibility, through a critical review of Web Content Accessibility Guidelines (WCAG), each design principle, guideline, check point and success criterion were identified and presented in detail. Limitations in WCAG were identified in terms of both Accessibility Guidelines and efforts made by web developers and machine auditing tools. As the investigation progressed, a shift in focus occurred towards an emerging web app technology known as PWAs. PWAs rely on a blend of cutting-edge technologies such as service workers, app shell architecture, web app manifest, and caching storage API for offline functionality. In a parallel critical evaluation, the advantages and limitations of adopting this innovative technology were explored. A sample of PWA websites and Ecommerce platforms was selected, and a comparative analysis against their non-PWA counterparts was manually conducted employing popular accessibility evaluation and auditing tools. Valuable insights regarding the accessibility of PWAs compared to non-PWA websites were derived following sample collection and a descriptive analysis utilizing Jupyter and Python. This study underscores the imperative role of accessibility in the web landscape. During the systematic literature review (SLR) in SEO, the necessity of SEO techniques and technologies for the correct presence of E-commerces on the World Wide Web and for higher rankings in the Search Engine Result Pages (SERPs) is established. The research is not limited to SEO Techniques; instead, the use of existing web technologies to boost SEO is suggested. For the purpose of the research, prototype tools in PHP and Python with crawler-like features were developed. These tools are designed to analyze targeted web pages, extracting valuable insights into the SEO techniques utilized on those pages. To simulate real-world conditions, these tools were connected to APIs, allowing them to retrieve additional metrics such as the number of backlinks, Domain Authority (DA), keywords, search ranking positions, responsive design, and website speed. Moreover, a pre-trained model was engineered through the integration of ML and predictive algorithms into the software. This model facilitated the precise prediction of the requisite number of backlinks and DA essential for a website to attain firstpage ranking in search results, all while considering the intricacies of its competitive landscape. These particular tools have been released as open-source on GitHub, providing non-specialized SEO teams and business owners with the opportunity to implement SEO strategies and observe results through the software's recommendations. To confirm the tools' effectiveness and obtain more concrete insights into which SEO techniques are more efficient, case studies were conducted on specific domains within live E-commerce platforms. Following the research, the rapid growth of the mobile phone industry was observed, leading to the exploration of alternative methods for developing web apps that are more mobile-friendly. One technology that captured the interest of webmasters was AMP. By utilizing existing technologies and imposing certain restrictions, AMP aimed to create pre-loaded web pages with advantages in terms of load time. These pages not only offered a reduction in page size but also improved rankings in search results, resulting in increased organic traffic due to their speed and user-friendliness. To ascertain whether the promised speed and ranking benefits are indeed delivered by AMP, a comparative analysis on the pages of a live E-commerce site was conducted in comparison to the corresponding pages of the same E-commerce site created using AMP technology.