Rating Prediction based on Optimal Review Topics: A Proposed Latent Factors-Optimal Topics Hybrid Approach

Rating prediction is an inevitable problem which recommender systems (RS) need to address. Its goal is to accurately predict the rating a user will assign to a particular item. Predictions which utilize numerical ratings and review texts are biased and have low accuracy. Also, existing topic-based rating prediction approaches focus on finding the most preferred items through the identification of latent topics expressed in users’ review texts. Even though the latent topics seem to represent most user review texts, they do not necessarily capture each user’s preferences. The goal of this work is then to develop a more accurate model by considering product review texts analysis so as to gain additional preference knowledge. Hence, a hybrid algorithm that optimizes the latent topics is proposed.  Specifically, the proposed approach finds appropriate weights for the topics of each review text. Rating prediction is critical task for RS because slight performance enhancement of the prediction accuracy results into significant improvements in recommendations. Experimental evaluation over real-world datasets revealed performance improvements of the proposed approach compared to alternative models. The proposed model can be used by RS in various domain such as e-learning, movie and hotel rating

Computation in Complex Networks

Complex networks are one of the most challenging research focuses of disciplines, including physics, mathematics, biology, medicine, engineering, and computer science, among others. The interest in complex networks is increasingly growing, due to their ability to model several daily life systems, such as technology networks, the Internet, and communication, chemical, neural, social, political and financial networks. The Special Issue “Computation in Complex Networks" of Entropy offers a multidisciplinary view on how some complex systems behave, providing a collection of original and high-quality papers within the research fields of: • Community detection • Complex network modelling • Complex network analysis • Node classification • Information spreading and control • Network robustness • Social networks • Network medicin

The application of recommender systems in education: a systematic literature review

O crescente interesse em pesquisas sobre sistemas de recomendações educacionais tem motivado o surgimento de novas técnicas e modelos nos últimos anos. Entretanto, as informações existentes sobre a diversidade de mecanismos utilizados para a produção de recomendações no contexto educacional são limitadas. Diante disso, este artigo apresenta uma revisão sistemática da literatura que sintetiza o conhecimento disponível sobre a forma que os recomendadores educacionais produzem as recomendações. Para tal foram selecionados 20 artigos publicados entre 2015 e 2019 de 517 publicações científicas identificadas. Os resultados fornecem conclusões sobre como os recomendadores educacionais funcionam apresentando um panorama das técnicas, entradas e saídas desses sistemas nas pesquisas mais recentes.The growing interest in educational recommender systems has motivated the emergence of new techniques and models in recent years. Despite this, there is limited information on a variety of mechanisms used by such systems to produce recommendations in the educational context. Therefore, this paper presents a systematic literature review that summarizes the available knowledge on the operation of educational recommender systems. Through the execution of the systematic review, 20 research papers published between 2015 and 2019 were selected from an initial set of 517 studies. The results provide findings regarding how educational recommenders work by presenting a panorama of the techniques, inputs and outputs of these systems in the most recent research.Facultad de Informátic

AI in Learning: Designing the Future

AI (Artificial Intelligence) is predicted to radically change teaching and learning in both schools and industry causing radical disruption of work. AI can support well-being initiatives and lifelong learning but educational institutions and companies need to take the changing technology into account. Moving towards AI supported by digital tools requires a dramatic shift in the concept of learning, expertise and the businesses built off of it. Based on the latest research on AI and how it is changing learning and education, this book will focus on the enormous opportunities to expand educational settings with AI for learning in and beyond the traditional classroom. This open access book also introduces ethical challenges related to learning and education, while connecting human learning and machine learning. This book will be of use to a variety of readers, including researchers, AI users, companies and policy makers

Proceedings of the Salford Postgraduate Annual Research Conference (SPARC) 2011

These proceedings bring together a selection of papers from the 2011 Salford Postgraduate Annual Research Conference(SPARC). It includes papers from PhD students in the arts and social sciences, business, computing, science and engineering, education, environment, built environment and health sciences. Contributions from Salford researchers are published here alongside papers from students at the Universities of Anglia Ruskin, Birmingham City, Chester,De Montfort, Exeter, Leeds, Liverpool, Liverpool John Moores and Manchester

AI in Learning: Designing the Future

AI (Artificial Intelligence) is predicted to radically change teaching and learning in both schools and industry causing radical disruption of work. AI can support well-being initiatives and lifelong learning but educational institutions and companies need to take the changing technology into account. Moving towards AI supported by digital tools requires a dramatic shift in the concept of learning, expertise and the businesses built off of it. Based on the latest research on AI and how it is changing learning and education, this book will focus on the enormous opportunities to expand educational settings with AI for learning in and beyond the traditional classroom. This open access book also introduces ethical challenges related to learning and education, while connecting human learning and machine learning. This book will be of use to a variety of readers, including researchers, AI users, companies and policy makers

Tracking the Temporal-Evolution of Supernova Bubbles in Numerical Simulations

The study of low-dimensional, noisy manifolds embedded in a higher dimensional space has been extremely useful in many applications, from the chemical analysis of multi-phase flows to simulations of galactic mergers. Building a probabilistic model of the manifolds has helped in describing their essential properties and how they vary in space. However, when the manifold is evolving through time, a joint spatio-temporal modelling is needed, in order to fully comprehend its nature. We propose a first-order Markovian process that propagates the spatial probabilistic model of a manifold at fixed time, to its adjacent temporal stages. The proposed methodology is demonstrated using a particle simulation of an interacting dwarf galaxy to describe the evolution of a cavity generated by a Supernov