Integrating Quality Criteria in a Fuzzy Linguistic Recommender System for Digital Libraries

Recommender systems can be used in an academic environment to assist users in their decision making processes to find relevant information. In the literature we can find proposals based in user’ profile or in item’ profile, however they do not take into account the quality of items. In this work we propose the combination of item’ relevance for a user with its quality in order to generate more profitable and accurate recommendations. The system measures item quality and takes it into account as new factor in the recommendation process. We have developed the system adopting a fuzzy linguistic approach.Projects TIN2010-17876, TIC5299 y TIC-599

Relational social recommendation: Application to the academic domain

This paper outlines RSR, a relational social recommendation approach applied to a social graph comprised of relational entity profiles. RSR uses information extraction and learning methods to obtain relational facts about persons of interest from the Web, and generates an associative entity-relation social network from their extracted personal profiles. As a case study, we consider the task of peer recommendation at scientific conferences. Given a social graph of scholars, RSR employs graph similarity measures to rank conference participants by their relatedness to a user. Unlike other recommender systems that perform social rankings, RSR provides the user with detailed supporting explanations in the form of relational connecting paths. In a set of user studies, we collected feedbacks from participants onsite of scientific conferences, pertaining to RSR quality of recommendations and explanations. The feedbacks indicate that users appreciate and benefit from RSR explainability features. The feedbacks further indicate on recommendation serendipity using RSR, having it recommend persons of interest who are not apriori known to the user, oftentimes exposing surprising inter-personal associations. Finally, we outline and assess potential gains in recommendation relevance and serendipity using path-based relational learning within RSR

Ontology-based process for recommending health websites

Website content quality is particularly relevant in the health domain. A common user needs to retrieve health information that is precise, reliable and relevant to his/her profile. Website recommendation systems are an aid to get high quality health-related web sites according to the user's needs. However, in practice, it is not always evident how to describe recommendation criteria for health website. The goal of this paper is to describe, by an ontology network, the criteria used by a health website recommendation process. This ontology network conceptualizes the different domains that are involved in the Salus Recommendation Project as a set of interrelated ontologies.Publicado en IFIP Advances in Information and Communication Technology book series (IFIPAICT, vol. 341).Laboratorio de Investigación y Formación en Informática Avanzad

Ontology-based process for recommending health websites

Website content quality is particularly relevant in the health domain. A common user needs to retrieve health information that is precise, reliable and relevant to his/her profile. Website recommendation systems are an aid to get high quality health-related web sites according to the user's needs. However, in practice, it is not always evident how to describe recommendation criteria for health website. The goal of this paper is to describe, by an ontology network, the criteria used by a health website recommendation process. This ontology network conceptualizes the different domains that are involved in the Salus Recommendation Project as a set of interrelated ontologies.Publicado en IFIP Advances in Information and Communication Technology book series (IFIPAICT, vol. 341).Laboratorio de Investigación y Formación en Informática Avanzad

TEST ENVIRONMENT FOR INTERACTIVE MOBILE TV SERVICES BASED ON DVB-H

La televisión móvil basada en el estándar DVB-H no ha tenido una aceptación semejante a la de la televisión convencional, lo anterior debido a dificultades propias del estándar y a características de despliegue del servicio. Las problemáticas de la televisión móvil se resumen en: necesidad de una red bidireccional alterna o canal de retorno para el consumo de servicios, el tiempo de salto de un canal a otro, el bajo tiempo promedio de uso diario de la televisión móvil, los pocos dispositivos que soportan el estándar y la no existencia de un middleware para el desarrollo de aplicaciones interactivas de televisión móvil. El presente artículo plantea un entorno de despliegue y pruebas para servicios interactivos de televisión móvil, que busca responder a los anteriores problemas. El entorno propuesto tiene en cuenta aspectos como la convergencia de servicios en redes WLAN, el uso falcsonomías para recomendación de contenidos y la red celular como canal de retorno o canal de consumo de servicios.Mobile TV based on DVB-H has not had an acceptance similar to the conventional television, above due to difficulties of the standard and the characteristics of service deployment. The problems of mobile TV are summarized in: the need to have a return channel for the consumption of services, the time to switch from one channel to another, the few devices that support the standard and the absence of a middleware for the development of Mobile TV interactive applications. This paper presents a testing and deployment environment for interactive mobile TV services, which wants to respond to the above problems. The proposed environment takes into account aspects such as: the convergence of services on WLANs, the use falksonomies for content recommendation and the cellular network as a return channel or channel service consumption

Optimizing E-Management Using Web Data Mining

Today, one of the biggest challenges that E-management systems face is the explosive growth of operating data and to use this data to enhance services. Web usage mining has emerged as an important technique to provide useful management information from user's Web data. One of the areas where such information is needed is the Web-based academic digital libraries. A digital library (D-library) is an information resource system to store resources in digital format and provide access to users through the network. Academic libraries offer a huge amount of information resources, these information resources overwhelm students and makes it difficult for them to access to relevant information. Proposed solutions to alleviate this issue emphasize the need to build Web recommender systems that make it possible to offer each student with a list of resources that they would be interested in. Collaborative filtering is the most successful technique used to offer recommendations to users. Collaborative filtering provides recommendations according to the user relevance feedback that tells the system their preferences. Most recent work on D-library recommender systems uses explicit feedback. Explicit feedback requires students to rate resources which make the recommendation process not realistic because few students are willing to provide their interests explicitly. Thus, collaborative filtering suffers from “data sparsity” problem. In response to this problem, the study proposed a Web usage mining framework to alleviate the sparsity problem. The framework incorporates clustering mining technique and usage data in the recommendation process. Students perform different actions on D-library, in this study five different actions are identified, including printing, downloading, bookmarking, reading, and viewing the abstract. These actions provide the system with large quantities of implicit feedback data. The proposed framework also utilizes clustering data mining approach to reduce the sparsity problem. Furthermore, generating recommendations based on clusters produce better results because students belonging to the same cluster usually have similar interests. The proposed framework is divided into two main components: off-line and online components. The off-line component is comprised of two stages: data pre-processing and the derivation of student clusters. The online component is comprised of two stages: building student's profile and generating recommendations. The second stage consists of three steps, in the first step the target student profile is classified to the closest cluster profile using the cosine similarity measure. In the second phase, the Pearson correlation coefficient method is used to select the most similar students to the target student from the chosen cluster to serve as a source of prediction. Finally, a top-list of resources is presented. Using the Book-Crossing dataset the effectiveness of the proposed framework was evaluated based on sparsity level, and Mean Absolute Error (MAE) regarding accuracy. The proposed framework reduced the sparsity level between (0.07% and 26.71%) in the sub-matrices, whereas the sparsity level is between 99.79% and 78.81% using the proposed framework, and 99.86% (for the original matrix) before applying the proposed framework. The experimental results indicated that by using the proposed framework the performance is as much as 13.12% better than clustering-only explicit feedback data, and 21.14% better than the standard K Nearest Neighbours method. The overall results show that the proposed framework can alleviate the Sparsity problem resulting in improving the accuracy of the recommendations

Smart library model based on big data technologies

Предмет истраживања докторске дисертације је развој модела памет не библиотеке заснованог на big data технологијама и сервисима. Централни истраживачки проблем разматран у раду је развој big data инфраструктуре и сервиса паметне библиотеке који омогућавају интелигентну претрагу и препоруку библиотечког садржаја. Посебан циљ рада је да испита могућност интеграције развијеног модела са паметним образовним окружењима у циљу унапређе ња квалитета образовног процеса. У докторској дисертацији је представљен модел паметне библиотеке као интегралног дела образовног система који може да побољша квалитет и свеобухватност наставних ресурса и повећа мотивацију у процесу учења препоручивањем садржаја од интереса. Модел описан у раду омогућава примену big data система за анализу, обраду и визуализацију података прикупљених из различитих извора и обухвата њихову интеграцију у паметну библиотеку. Циљ развоја паметних библиотека је да се унапреде библиотечки пословни процеси и да се корисницима пруже иновативни сервиси за претрагу и коришћење садржаја. У дисертацији се разматрају различите перспективе имплементације big data решења за паметне библиотеке као део континуираног образовног процеса, са посебним фокусом на интеграцију традиционалних система и big data технологија. Поред наведених компонената система, модел обухвата инфраструктуру и интеграцију система препоруке колаборативног филтрирања извора различитих података са big data технологијама. Модел је евалуиран кроз тестирање и мерење релевантних параметара перформанси који утичу на ефикасност предложеног модела.The subject of this doctoral dissertation research is the development of a smart library model based on big data technologies and services . The central research problem discussed in the thesis is the development of big data infrastr ucture and smart library services that enable intelligent searches and recommendations from the library content. A particular focus of the paper is an examination of the possibility of integrating the developed model into a smart educational environment in order to improve the quality of the educational process. The thesis presents a model of the smart library as an integral part of the educational system that would improve quality level and comprehesivness of learning resources and increase the motivation of its users through content aware recommendations. The model described in the thesis considers the possibilities of applying a big data system for the collection, analysis, processing and visualization of data from multiple sources, and the integration of data into the smart library . The goal of developing a smart library is to improve the library’s business process and to offer users innovative metho ds to search and content use. The thesis discusses the perspective of the implementation of a big data solu tion for smart libraries as a part of a continuous learning process with the aim of improving the results of library operations by integrating traditional systems with big data technology. In addition to the above system components, the model includes the infrastructure and integration of a recommender system for collaborative filtering by incorporating multiple sources of differential data with big data technologies. Within the evaluation of the model, testing and measurement of the relevant performance p arameters which influence the efficiency of the proposed model were carried out

Les systèmes de recommandations pour soutenir l'agentivité des enseignantes et des enseignants au collégial dans leur développement professionnel

L’objectif général de cette thèse est de contribuer au soutien du développement professionnel des enseignantes et des enseignants au collégial en investiguant leur agentivité avec le numérique. L’agentivité est définie ici comme la capacité à définir et à poursuivre des objectifs de développement professionnel. Cette recherche, qui met en œuvre une méthodologie d’expérimentation de devis, s’est déroulée en trois phases. Dans une première phase, nous avons investigué des besoins d’enseignantes du collégial en nous intéressant aux buts que devait soutenir une plateforme numérique soutenant l’exercice de l’agentivité. Pour y parvenir, trois ateliers de codesign ont été menés et analysés sous l’angle du modèle de l’expérience utilisateur de Hassenzahl (2003). Ces ateliers, inspirés des Future Workshop (Muller et Druin, 2012), ont permis d’identifier des buts motivationnels des participantes, c’est-à-dire ce qu’elles souhaitaient qu’une plateforme misant sur l’exercice d’agentivité puisse combler : faire du développement professionnel une priorité, poser un regard réflexif sur l’innovation, faciliter l’accès aux ressources, et faciliter les échanges et le partage. Les ateliers ont aussi permis d’identifier des buts fonctionnels et opérationnels, c’est-à-dire les fonctionnalités qui permettent de combler les besoins motivationnels. Parmi eux, plusieurs se sont révélés être liés aux systèmes de recommandations, qui sont des outils et techniques qui suggèrent les items les plus susceptibles d’intéresser un utilisateur (Ricci et al., 2015). C’est pourquoi une revue systématique de la littérature a été réalisée afin d’identifier notamment les techniques utilisées et les façons d’évaluer les systèmes de recommandations utilisés dans un contexte d’apprentissage. Dans cette deuxième phase de cette recherche doctorale, ce sont 56 articles scientifiques revus par les pairs, parus entre 2008 et 2018, qui ont été analysés sous trois grandes questions et une cinquantaine d’aspects. Ils ont permis d’orienter le développement de la plateforme, dont l’implantation et les améliorations ont constitué la troisième et dernière phase. Cette phase s’est déroulée en trois itérations de conception, intervention, analyse et amélioration. Durant chacune des itérations, les six participantes ont expérimenté la plateforme, répondu à un questionnaire et reçu une rétroaction personnalisée. Le questionnaire visait à analyser le potentiel de la plateforme pour répondre aux buts motivationnels identifiés à la première phase, à analyser la satisfaction à l’égard des ressources recommandées (Erdt et al., 2015), de même qu’à analyser l’expérience utilisateur (Hassenzahl, 2003). Pour des fins d’analyse, les réponses aux questionnaires ont été croisées avec les données entrées par le personnel enseignant dans la plateforme ainsi que les actions faites dans la plateforme et entrées au journal d’évènements. Cette analyse nous a permis d’observer une augmentation de la perception du soutien à l’agentivité des enseignantes, en particulier la capacité de la plateforme à faciliter l’accès aux ressources pour mieux connaitre les occasions de développement professionnel et pour accéder aux activités de développement professionnel les plus appropriées. Au fil des itérations, nous avons également observé une augmentation de la satisfaction à l’égard des ressources recommandées grâce à une approche basée sur le contenu. La variation de la moyenne globale des aspects hédoniques et pragmatiques est elle aussi positive à chacune des itérations. C’est dire que le codesign d’un environnement numérique dans le cadre d’une recherche avec des enseignantes a été une forme de développement professionnel, et le codesign en contexte de développement professionnel a favorisé l’exercice de l’agentivité des participantes. Le design itératif de cette recherche a contribué à faire du développement professionnel une priorité, un besoin identifié durant la phase de codesign. Cette étude a permis d’identifier et de mettre en œuvre des pistes permettant de faciliter l’exercice de l’agentivité des enseignantes et des enseignants.The general objective of this thesis is to contribute to supporting the professional development of college teachers by investigating their agency with the support of digital technology. Agency is defined here as the ability to define and pursue professional development goals. This research, which implements a design-based research methodology, was carried out in three phases. In the first phase, we investigated the needs of college teachers by looking at the goals that a digital platform supporting the exercise of agency should support. To achieve this, three codesign workshops were conducted and analyzed from the perspective of Hassenzahl's (2003) user experience model. These workshops, inspired by the Future Workshop (Muller & Druin, 2012), made it possible to identify the participants' “be-goals”, i.e., what they wanted a platform based on the exercise of agency to achieve: to make professional development a priority, to take a reflective look at innovation, to facilitate access to resources, and to facilitate exchanges and sharing. The workshops also made it possible to identify “do-goals” and “motor-goals”, i.e., the functionalities that make it possible to meet be-goals. Among them, several were found to be related to recommendation systems, which are tools and techniques that suggest the items most likely to interest a user (Ricci et al., 2015). For this reason, a systematic review of the literature was conducted to identify the techniques used and the ways to evaluate the recommendations systems used in a learning context. In this second phase of this doctoral research, 56 peer-reviewed scientific articles, published between 2008 and 2018, were analyzed under three main questions and about 50 aspects. They were used to help guide the development of the platform, whose implementation and improvements constituted the third and final phase. This phase took place in three iterations of design/implementation, intervention, analysis and improvement. During each iteration, the six participants experimented with the platform, answered a questionnaire and received personalized feedback. The questionnaire aimed to analyze the platform's potential to meet the motivational goals (“be-goals”) identified in the first phase, to analyze satisfaction with the recommended resources (Erdt et al., 2015), and to analyze the user experience (Hassenzahl, 2003). For analysis purposes, the responses to the questionnaires were cross-referenced with the data entered by the teaching staff in the platform as well as the actions taken in the platform and entries in the event log. This analysis allowed us to observe an increase in the perception of support for teachers' agency, in particular the platform's ability to facilitate access to resources to learn more about professional development opportunities and to access the most appropriate professional development activities. Over the iterations we also observed an increase in satisfaction with the recommended resources through a content-based approach. The variation in the overall average of the hedonic and pragmatic aspects is also positive in each iteration. This means that codesigning a digital environment in the context of research with female teachers was a form of professional development, and codesigning in a professional development context promoted the participants' exercise of agency. The iterative design of this research contributed to making professional development a priority, a need identified during the codesign phase. This study made it possible to identify and implement avenues to facilitate teachers' exercise of agency