Engaging end-user driven recommender systems: personalization through web augmentation

In the past decades recommender systems have become a powerful tool to improve personalization on the Web. Yet, many popular websites lack such functionality, its implementation usually requires certain technical skills, and, above all, its introduction is beyond the scope and control of end-users. To alleviate these problems, this paper presents a novel tool to empower end-users without programming skills, without any involvement of website providers, to embed personalized recommendations of items into arbitrary websites on client-side. For this we have developed a generic meta-model to capture recommender system configuration parameters in general as well as in a web augmentation context. Thereupon, we have implemented a wizard in the form of an easy-to-use browser plug-in, allowing the generation of so-called user scripts, which are executed in the browser to engage collaborative filtering functionality from a provided external rest service. We discuss functionality and limitations of the approach, and in a study with end-users we assess the usability and show its suitability for combining recommender systems with web augmentation techniques, aiming to empower end-users to implement controllable recommender applications for a more personalized browsing experience.Fil: Wischenbart, Martin. Johannes Kepler University Linz; AustriaFil: Firmenich, Sergio Damian. Universidad Nacional de La Plata. Facultad de Informática. Laboratorio de Investigación y Formación en Informática Avanzada; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; ArgentinaFil: Rossi, Gustavo Héctor. Universidad Nacional de La Plata. Facultad de Informática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; ArgentinaFil: Bosetti, Gabriela Alejandra. Universidad Nacional de La Plata. Facultad de Informática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; ArgentinaFil: Kapsammer, Elisabeth. Johannes Kepler University Linz; Austri

A Model-driven Approach for Empowering Advance Web Augmentation From Client-side to Server-side Support

Websites augmentations have been adopted as a mean for improving the User Experience of applications that often are not owned by the user. The augmentations alter the page in order to add, modify and even remove its content pursuing the satisfaction of a user’s need. However, these augmentations are limited to page modification or transcluding content from another site on Internet. Moreover, advance server-side based augmentations have been released only by developers because of the required technical skill for the task. In this work, we have presented a novel approach for designing Web Augmentation coping client-side and server side using a Model-Driven Web Engineering approach. The approach rises the abstraction level for server side developments allowing end-users to design, and even implement the new functionalities. Additionally, the approach uses advance separation of concern principles thus we provide a set of tools for designing the composition of the core application and the augmentation. We show as running example an augmentation that introduces a site community’s review support upon an agriculture e-commerce site.European Union Horizon 2020 No.62149Ministerio de Ciencia e Innovación 2016-76956-C3-2-R (POLOLAS

Recommender Systems in E-commerce

E-commerce recommender systems are becoming increasingly important in the current digital world. They are used to personalize user experience, help customers find what they need quickly and efficiently, and increase revenue for the business. However, there are several challenges associated with big data-based e-commerce recommender systems. These challenges include limited resources, data validity period, cold start, long tail problem, scalability. In this paper, we discuss the challenges and potential solutions to overcome these challenges. We also discuss the different types of e-commerce recommender systems, their advantages, and disadvantages. We conclude with some future research directions to improve the performance of e-commerce recommender systems

Design of an E-learning system using semantic information and cloud computing technologies

Humanity is currently suffering from many difficult problems that threaten the life and survival of the human race. It is very easy for all mankind to be affected, directly or indirectly, by these problems. Education is a key solution for most of them. In our thesis we tried to make use of current technologies to enhance and ease the learning process. We have designed an e-learning system based on semantic information and cloud computing, in addition to many other technologies that contribute to improving the educational process and raising the level of students. The design was built after much research on useful technology, its types, and examples of actual systems that were previously discussed by other researchers. In addition to the proposed design, an algorithm was implemented to identify topics found in large textual educational resources. It was tested and proved to be efficient against other methods. The algorithm has the ability of extracting the main topics from textual learning resources, linking related resources and generating interactive dynamic knowledge graphs. This algorithm accurately and efficiently accomplishes those tasks even for bigger books. We used Wikipedia Miner, TextRank, and Gensim within our algorithm. Our algorithm‘s accuracy was evaluated against Gensim, largely improving its accuracy. Augmenting the system design with the implemented algorithm will produce many useful services for improving the learning process such as: identifying main topics of big textual learning resources automatically and connecting them to other well defined concepts from Wikipedia, enriching current learning resources with semantic information from external sources, providing student with browsable dynamic interactive knowledge graphs, and making use of learning groups to encourage students to share their learning experiences and feedback with other learners.Programa de Doctorado en Ingeniería Telemática por la Universidad Carlos III de MadridPresidente: Luis Sánchez Fernández.- Secretario: Luis de la Fuente Valentín.- Vocal: Norberto Fernández Garcí

Media Analytics for Personalization in Advertisement

In the realm of advertising, strategic placement and presentation of advertisements are crucial for attracting potential clients. Media companies employ various tactics, such as visually appealing features and vibrant colors, to capture the attention of consumers. However, achieving this objective is not always straightforward, as some advertising strategies may be perceived as irrelevant or disturbing by recipients. This Master's thesis aims to explore the relationship between audience interaction and the perception of advertisements on media platforms, with the overarching goal of enhancing advertising effectiveness and addressing ethical concerns associated with targeted advertising. To delve into this topic comprehensively, this study utilizes real-time data provided by Amedia, one of the largest media companies in Norway. Through an extensive analysis of this real-world data, the research aims to explore the correlation between audience interaction and the perception of advertisements on media platforms. This investigation involves the extraction of relevant features from advertisement images, leading to the creation of a new dataset. Concurrently, predictive machine learning models are developed to gain insights into effective advertising strategies for media companies, with a focus on personalization. Furthermore, a comprehensive user study is conducted to gain insights into user behavior within media platform advertisements. By uncovering the interplay between visual features, user behavior, and advertising effectiveness, this research contributes to improving personalized advertising strategies in the context of media companies.Masteroppgåve i informasjonsvitskapINFO390MASV-INF

A Web-based Model-driven Platform for Web Augmentation

The emergence of Web personalization allowed introducing improvements to an application that runs as a black box just considering those perceivable behaviors by the end-user. In spite of which mechanisms (personalization, customization, etc.) that a particular application supports; it is not realistic to state that any application, being idealized by few people (e.g., its owners), covers every single user´s needs. In this sense, users may have unsatisfied requirements. Nowadays available Web augmentations are making full use of server-side capabilities for meeting requirements. We present a Web Augmentation modeling approach contemplating a client-server application that hides the back-end complexity to users. In this work we present a Web CASE tool to model server-side behavior for Web augmentation. This tool provides a full web-based experience for designing and running Web augmentations that requires client and server-side components.Fil: Urbieta, Mario Matías. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina. Universidad Nacional de La Plata. Facultad de Informática; ArgentinaFil: Mahl, Franco. Universidad Nacional de La Plata. Facultad de Informática; ArgentinaFil: Rossi, Gustavo Héctor. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina. Universidad Nacional de La Plata. Facultad de Informática; ArgentinaFil: Bosetti, Gabriela Alejandra. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina. Universidad Nacional de La Plata. Facultad de Informática; Argentina15th International Conference on Web Information Systems and TechnologiesVienaAustriaInstituto de Sistemas y Tecnologías de Información, Control y ComunicaciónUniversidad Técnica de Vien