29,586 research outputs found
Comparative Analysis of Functionality and Aspects for Hybrid Recommender Systems
Recommender systems are gradually becoming the backbone of profitable business which interact with users mainly on the web stack. These systems are privileged to have large amounts of user interaction data used to improve them. The systems utilize machine learning and data mining techniques to determine products and features to suggest different users correctly. This is an essential function since offering the right product at the right time might result in increased revenue. This paper gives focus on the importance of different kinds of hybrid recommenders. First, by explaining the various types of recommenders in use, then showing the need for hybrid systems and the multiple kinds before giving a comparative analysis of each of these. Keeping in mind that content-based, as well as collaborative filtering systems, are widely used, research is comparatively done with a keen interest on how this measures up to hybrid recommender systems
Recommender Systems
The ongoing rapid expansion of the Internet greatly increases the necessity
of effective recommender systems for filtering the abundant information.
Extensive research for recommender systems is conducted by a broad range of
communities including social and computer scientists, physicists, and
interdisciplinary researchers. Despite substantial theoretical and practical
achievements, unification and comparison of different approaches are lacking,
which impedes further advances. In this article, we review recent developments
in recommender systems and discuss the major challenges. We compare and
evaluate available algorithms and examine their roles in the future
developments. In addition to algorithms, physical aspects are described to
illustrate macroscopic behavior of recommender systems. Potential impacts and
future directions are discussed. We emphasize that recommendation has a great
scientific depth and combines diverse research fields which makes it of
interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports
Psychological elements explaining the consumer's adoption and use of a website recommendation system: A theoretical framework proposal
The purpose of this paper is to understand, with an emphasis on the psychological perspective of the research problem, the consumer's adoption and use of a certain web site recommendation system as well as the main psychological outcomes involved. The approach takes the form of theoretical modelling. Findings: A conceptual model is proposed and discussed. A total of 20 research propositions are theoretically analyzed and justified. Research limitations/implications: The theoretical discussion developed here is not empirically validated. This represents an opportunity for future research. Practical implications: The ideas extracted from the discussion of the conceptual model should be a help for recommendation systems designers and web site managers, so that they may be more aware, when working with such systems, of the psychological process consumers undergo when interacting with them. In this regard, numerous practical reflections and suggestions are presented
Effective Virtual Teams for New Product Development
At present, the existing literature shows that the factors which influence the effectiveness of virtual teams for new product development are still ambiguous. To address this problem, a research design was developed, which includes detailed literature review, preliminary model and field survey. From literature review, the factors which influence the effectiveness of virtual teams are identified and these factors are modified using a field survey. The relationship between knowledge workers (people), process and technology in virtual teams is explored in this study. The results of the study suggest that technology and process are tightly correlated and need to be considered early in virtual teams. The use of software as a service, web solution, report generator and tracking system should be incorporated for effectiveness virtual teams
How does intellectual capital align with cyber security?
Purpose – To position the preservation and protection of intellectual capital as a cyber security concern. We outline the security requirements of intellectual capital to help Boards of Directors and executive management teams to understand their responsibilities and accountabilities in this respect.Design/Methodology/Approach – The research methodology is desk research. In other words, we gathered facts and existing research publications that helped us to define key terms, to formulate arguments to convince BoDs of the need to secure their intellectual capital, and to outline actions to be taken by BoDs to do so.Findings – Intellectual capital, as a valuable business resource, is related to information, knowledge and cyber security. Hence, preservation thereof is also related to cyber security governance, and merits attention from boards of directors.Implications – This paper clarifies boards of directors’ intellectual capital governance responsibilities, which encompass information, knowledge and cyber security governance.Social Implications – If boards of directors know how to embrace their intellectual capital governance responsibilities, this will help to ensure that such intellectual capital is preserved and secured.Practical Implications – We hope that boards of directors will benefit from our clarifications, and especially from the positioning of intellectual capital in cyber space.Originality/Value – This paper extends a previous paper published by Von Solms and Von Solms (2018), which clarified the key terms of information and cyber security, and the governance thereof. The originality and value is the focus on the securing of intellectual capital, a topic that has not yet received a great deal of attention from cyber security researchers
Automatic management tool for attribution and monitorization of projects/internships
No último ano académico, os estudantes do ISEP necessitam de realizar um projeto final para
obtenção do grau académico que pretendem alcançar. O ISEP fornece uma plataforma digital
onde é possível visualizar todos os projetos que os alunos se podem candidatar. Apesar das
vantagens que a plataforma digital traz, esta também possui alguns problemas, nomeadamente
a difícil escolha de projetos adequados ao estudante devido à excessiva oferta e falta de
mecanismos de filtragem. Para além disso, existe também uma indecisão acrescida para
selecionar um supervisor que seja compatível para o projeto selecionado.
Tendo o aluno escolhido o projeto e o supervisor, dá-se início à fase de monitorização do
mesmo, que possui também os seus problemas, como o uso de diversas ferramentas que
posteriormente levam a possíveis problemas de comunicação e dificuldade em manter um
histórico de versões do trabalho desenvolvido.
De forma a responder aos problemas mencionados, realizou-se um estudo aprofundado dos
tópicos de sistemas de recomendação aplicados a Machine Learning e Learning Management
Systems. Para cada um desses grandes temas, foram analisados sistemas semelhantes capazes
de solucionar o problema proposto, tais como sistemas de recomendação desenvolvidos em
artigos científicos, aplicações comerciais e ferramentas como o ChatGPT.
Através da análise do estado da arte, concluiu-se que a solução para os problemas propostos
seria a criação de uma aplicação Web para alunos e supervisores, que juntasse as duas
temáticas analisadas. O sistema de recomendação desenvolvido possui filtragem colaborativa
com factorização de matrizes, e filtragem por conteúdo com semelhança de cossenos. As
tecnologias utilizadas no sistema centram-se em Python no back-end (com o uso de TensorFlow
e NumPy para funcionalidades de Machine Learning) e Svelte no front-end. O sistema foi
inspirado numa arquitetura em microsserviços em que cada serviço é representado pelo seu
próprio contentor de Docker, e disponibilizado ao público através de um domínio público.
O sistema foi avaliado através de três métricas: performance, confiabilidade e usabilidade. Foi
utilizada a ferramenta Quantitative Evaluation Framework para definir dimensões, fatores e
requisitos(e respetivas pontuações). Os estudantes que testaram a solução avaliaram o sistema
de recomendação com um valor de aproximadamente 7 numa escala de 1 a 10, e os valores de
precision, recall, false positive rate e F-Measure foram avaliados em 0.51, 0.71, 0.23 e 0.59
respetivamente. Adicionalmente, ambos os grupos classificaram a aplicação como intuitiva e
de fácil utilização, com resultados a rondar o 8 numa escala de 1 em 10.In the last academic year, students at ISEP need to complete a final project to obtain the
academic degree they aim to achieve. ISEP provides a digital platform where all the projects
that students can apply for can be viewed. Besides the advantages this platform has, it also
brings some problems, such as the difficult selection of projects suited for the student due to
the excessive offering and lack of filtering mechanisms. Additionally, there is also increased
difficulty in selecting a supervisor compatible with their project.
Once the student has chosen the project and the supervisor, the monitoring phase begins,
which also has its issues, such as using various tools that may lead to potential communication
problems and difficulty in maintaining a version history of the work done.
To address the mentioned problems, an in-depth study of recommendation systems applied to
Machine Learning and Learning Management Systems was conducted. For each of these
themes, similar systems that could solve the proposed problem were analysed, such as
recommendation systems developed in scientific papers, commercial applications, and tools
like ChatGPT.
Through the analysis of the state of the art, it was concluded that the solution to the proposed
problems would be the creation of a web application for students and supervisors that
combines the two analysed themes. The developed recommendation system uses collaborative
filtering with matrix factorization and content-based filtering with cosine similarity. The
technologies used in the system are centred around Python on the backend (with the use of
TensorFlow and NumPy for Machine Learning functionalities) and Svelte on the frontend. The
system was inspired by a microservices architecture, where each service is represented by its
own Docker container, and it was made available online through a public domain.
The system was evaluated through performance, reliability, and usability. The Quantitative
Evaluation Framework tool was used to define dimensions, factors, and requirements (and their
respective scores). The students who tested the solution rated the recommendation system
with a value of approximately 7 on a scale of 1 to 10, and the precision, recall, false positive
rate, and F-Measure values were evaluated at 0.51, 0.71, 0.23, and 0.59, respectively.
Additionally, both groups rated the application as intuitive and easy to use, with ratings around
8 on a scale of 1 to 10
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