2,163 research outputs found
Personalized question-based cybersecurity recommendation systems
En ces temps de pandémie Covid19, une énorme quantité de l’activité humaine est modifiée pour se faire à distance, notamment par des moyens électroniques. Cela rend plusieurs personnes et services vulnérables aux cyberattaques, d’où le besoin d’une éducation généralisée ou du moins accessible sur la cybersécurité. De nombreux efforts sont entrepris par les chercheurs, le gouvernement et les entreprises pour protéger et assurer la sécurité des individus contre les pirates et les cybercriminels. En raison du rôle important joué par les systèmes de recommandation dans la vie quotidienne de l'utilisateur, il est intéressant de voir comment nous pouvons combiner les systèmes de cybersécurité et de recommandation en tant que solutions alternatives pour aider les utilisateurs à comprendre les cyberattaques auxquelles ils peuvent être confrontés. Les systèmes de recommandation sont couramment utilisés par le commerce électronique, les réseaux sociaux et les plateformes de voyage, et ils sont basés sur des techniques de systèmes de recommandation traditionnels.
Au vu des faits mentionnés ci-dessus, et le besoin de protéger les internautes, il devient important de fournir un système personnalisé, qui permet de partager les problèmes, d'interagir avec un système et de trouver des recommandations.
Pour cela, ce travail propose « Cyberhelper », un système de recommandation de cybersécurité personnalisé basé sur des questions pour la sensibilisation à la cybersécurité.
De plus, la plateforme proposée est équipée d'un algorithme hybride associé à trois différents algorithmes basés sur la connaissance, les utilisateurs et le contenu qui garantit une recommandation personnalisée optimale en fonction du modèle utilisateur et du contexte. Les résultats expérimentaux montrent que la précision obtenue en appliquant l'algorithme proposé est bien supérieure à la précision obtenue en utilisant d'autres mécanismes de système de recommandation traditionnels. Les résultats suggèrent également qu'en adoptant l'approche proposée, chaque utilisateur peut avoir une expérience utilisateur unique, ce qui peut l'aider à comprendre l'environnement de cybersécurité.With the proliferation of the virtual universe and the multitude of services provided by the World Wide Web, a major concern arises: Security and privacy have never been more in jeopardy. Nowadays, with the Covid 19 pandemic, the world faces a new reality that pushed the majority of the workforce to telecommute. This thereby creates new vulnerabilities for cyber attackers to exploit. It’s important now more than ever, to educate and offer guidance towards good cybersecurity hygiene. In this context, a major effort has been dedicated by researchers, governments, and businesses alike to protect people online against hackers and cybercriminals.
With a focus on strengthening the weakest link in the cybersecurity chain which is the human being, educational and awareness-raising tools have been put to use. However, most researchers focus on the “one size fits all” solutions which do not focus on the intricacies of individuals. This work aims to overcome that by contributing a personalized question-based recommender system. Named “Cyberhelper”, this work benefits from an existing mature body of research on recommender system algorithms along with recent research on non-user-specific question-based recommenders.
The reported proof of concept holds potential for future work in adapting Cyberhelper as an everyday assistant for different types of users and different contexts
Deep Learning based Recommender System: A Survey and New Perspectives
With the ever-growing volume of online information, recommender systems have
been an effective strategy to overcome such information overload. The utility
of recommender systems cannot be overstated, given its widespread adoption in
many web applications, along with its potential impact to ameliorate many
problems related to over-choice. In recent years, deep learning has garnered
considerable interest in many research fields such as computer vision and
natural language processing, owing not only to stellar performance but also the
attractive property of learning feature representations from scratch. The
influence of deep learning is also pervasive, recently demonstrating its
effectiveness when applied to information retrieval and recommender systems
research. Evidently, the field of deep learning in recommender system is
flourishing. This article aims to provide a comprehensive review of recent
research efforts on deep learning based recommender systems. More concretely,
we provide and devise a taxonomy of deep learning based recommendation models,
along with providing a comprehensive summary of the state-of-the-art. Finally,
we expand on current trends and provide new perspectives pertaining to this new
exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys.
https://doi.acm.org/10.1145/328502
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
ChatGPT and Persuasive Technologies for the Management and Delivery of Personalized Recommendations in Hotel Hospitality
Recommender systems have become indispensable tools in the hotel hospitality
industry, enabling personalized and tailored experiences for guests. Recent
advancements in large language models (LLMs), such as ChatGPT, and persuasive
technologies, have opened new avenues for enhancing the effectiveness of those
systems. This paper explores the potential of integrating ChatGPT and
persuasive technologies for automating and improving hotel hospitality
recommender systems. First, we delve into the capabilities of ChatGPT, which
can understand and generate human-like text, enabling more accurate and
context-aware recommendations. We discuss the integration of ChatGPT into
recommender systems, highlighting the ability to analyze user preferences,
extract valuable insights from online reviews, and generate personalized
recommendations based on guest profiles. Second, we investigate the role of
persuasive technology in influencing user behavior and enhancing the persuasive
impact of hotel recommendations. By incorporating persuasive techniques, such
as social proof, scarcity and personalization, recommender systems can
effectively influence user decision-making and encourage desired actions, such
as booking a specific hotel or upgrading their room. To investigate the
efficacy of ChatGPT and persuasive technologies, we present a pilot experi-ment
with a case study involving a hotel recommender system. We aim to study the
impact of integrating ChatGPT and persua-sive techniques on user engagement,
satisfaction, and conversion rates. The preliminary results demonstrate the
potential of these technologies in enhancing the overall guest experience and
business performance. Overall, this paper contributes to the field of hotel
hospitality by exploring the synergistic relationship between LLMs and
persuasive technology in recommender systems, ultimately influencing guest
satisfaction and hotel revenue.Comment: 17 pages, 12 figure
Recommender Systems in Light of Big Data
The growth in the usage of the web, especially e-commerce website, has led to the development of recommender system (RS) which aims in personalizing the web content for each user and reducing the cognitive load of information on the user. However, as the world enters Big Data era and lives through the contemporary data explosion, the main goal of a RS becomes to provide millions of high quality recommendations in few seconds for the increasing number of users and items. One of the successful techniques of RSs is collaborative filtering (CF) which makes recommendations for users based on what other like-mind users had preferred. Despite its success, CF is facing some challenges posed by Big Data, such as: scalability, sparsity and cold start. As a consequence, new approaches of CF that overcome the existing problems have been studied such as Singular value decomposition (SVD). This paper surveys the literature of RSs and reviews the current state of RSs with the main concerns surrounding them due to Big Data. Furthermore, it investigates thoroughly SVD, one of the promising approaches expected to perform well in tackling Big Data challenges, and provides an implementation to it using some of the successful Big Data tools (i.e. Apache Hadoop and Spark). This implementation is intended to validate the applicability of, existing contributions to the field of, SVD-based RSs as well as validated the effectiveness of Hadoop and spark in developing large-scale systems. The implementation has been evaluated empirically by measuring mean absolute error which gave comparable results with other experiments conducted, previously by other researchers, on a relatively smaller data set and non-distributed environment. This proved the scalability of SVD-based RS and its applicability to Big Data
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