Multi-Agent-Based Recommender Systems: A Literature Review

Neto, Joaquim Bento Cavalcante; Morais, A. Jorge; Coelho, António Leça

doi:10.1007/978-981-16-2377-6_51

Cited by 5 publications

(4 citation statements)

References 33 publications

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“…is study uses word of mouth to express the possibility of effective cooperation. If z and Y collaborate directly, we may try to predict the likelihood of direct cooperation succeeding, which is known as direct credibility assessment and is given in percent [8]. At the same time, if there is one or more intermediate agents w (also known as recommended agents) between Z and Y, W can be used as the recommended agent to establish a link, and the relationship between Z and Y can be obtained directly through W. e recommendation credibility evaluation is the collection of information regarding a successful partnership.…”

Section: Basic Conceptsmentioning

confidence: 99%

Multi-Agent-Based Film Editing Collaboration System

Liang

2022

Computational Intelligence and Neuroscience

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In order to realize the effective cooperation between editor agents in the film and television editing collaboration system, it is analyzed that the state change of the film and television editing and production process is affected by the cross influence of multiple factors. A single agent can no longer satisfy the current film and television production. From the point of view of system theory, this article constructs the learner agent in the film and television editing system by introducing a new cooperation mechanism—the multi-agent collaborative system model. Collaboration and cooperation between multiple agents and the reinforcement learning between multiple editor agents are realized based on the film and television editing system between multiple agents. The operation mechanism of the separate organization is organized together, cooperates with each other and works in harmony to complete the collaborative effect of the film and television editing system, and can improve the interaction efficiency between the editor agents. Agent film and television editing’s cooperative learning approach allows for successful collaboration among editor agents. The Bayesian technique is utilized in this study to assess the likelihood of effective cooperation between two agents, and a trust model based on this method is presented, making up for the shortcomings of the existing collaborative learning system. The multi-agent collaboration system will be utilized for production in the film and television editing collaboration system. Many of the movie’s scenes and segments are created using computer technology special effects, giving viewers a very unique experience and a feast for the eyes and ears.

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Section: Basic Conceptsmentioning

confidence: 99%

Multi-Agent-Based Film Editing Collaboration System

Liang

2022

Computational Intelligence and Neuroscience

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“…D'autre part, la majorité des systèmes de recommandation se fonde sur la collecte d'information concernant un utilisateur (comme son historique de navigation ou ses interactions avec le site) pour l'adaptation du contenu et pour la personnalisation du parcours. Or, cela soulève un problème de confidentialité des données des utilisateurs selon [61]. Les auteurs dans [13] ont pu résoudre ce problème par la présentation d'une approche de recommandation basée sur les systèmes multi-agents, tout en gardant la confidentialité des données utilisateurs.…”

Section: Système D'apprentissage Adaptatif Collaboratifunclassified

“…Entre autres, ceci permet une meilleure personnalisation des parcours d'apprentissage [23,47,48]. D'autre part, les SMA ont montré leur grande capacité à orchestrer en temps réel un ensemble d'agents (ici représentant les utilisateurs de la plate-forme) [61]. L'orchestration des agents peut suivre 4 étapes selon le principe MAPE-K [53] : surveiller, analyser, planifier et exécuter.…”

Section: Méthodologieunclassified

Towards a semantic platform for adaptive and collaborative e-learning

Sabeima

Lamolle²,

Anghour³

et al. 2023

Revue Africaine De Recherche en Informatique Et Mathématiques Appliquées

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soumission à Episciences In the world of e-learning, learning systems have sought to adapt the user's profile and the content offered to them. However, from the point of view of collaboration between learners based on adaptation to the learner profile, this adaptation has not been sufficiently explored as an important aspect of the e-learning process. Adaptation will allow users with similar or very similar profiles to be grouped together to learn in harmony while maintaining motivation and commitment to learning. This should increase the success rate of learners. This will also allow us to reuse learning paths with good success rates for future recommendations to users with the same profile. In this paper, we focus on this aspect and propose a learning system that controls learning paths adapted to the users' profile and that allows collaborative learning of users in a synchronous way. After an overview of the existing work in the field of adaptive e-learning, we propose an architecture for the piloting of this type of collaborative adaptive learning based on ontologies and orchestrated by a multi-agent system. The latter is responsible for the piloting of learning paths, the recommendation of paths in collaborative or non-collaborative mode through communication between the different agents involved, and the management of events captured by the system. Dans le monde de la formation en ligne, les systèmes d'apprentissage ont cherché à proposer un contenu adapté à tout utilisateur selon son profil.Cette adaptation a comme objectif général de permettre à l'apprenant de tirer le meilleur parti du contenu exposé des ressources d'apprentissage.Cependant, du point de vue de la collaboration entre apprenants selon l'adaptation au profil des apprenants, cette adaptation n'a pas été suffisamment explorée comme un aspect important du processus d'apprentissage en ligne.L'adaptation va permettre de grouper des utilisateurs de profil similaire ou très proche pour apprendre en harmonie tout en gardant la motivation et l'engagement nécessaire afin d'augmenter le taux de réussite des apprenants. Cet état de fait doit permettre aussi de réutiliser certains parcours d'apprentissage avec de bons taux de réussite pour de futures recommandations aux utilisateurs ayant le même profil. Dans cet article, nous mettons l'accent sur cet aspect et proposons un système d'apprentissage qui recommande des parcours d'apprentissage adaptés au profil des utilisateurs et qui permet un apprentissage collaboratif des utilisateurs de façon synchrone. Après un tour d'horizon de l'existant dans le domaine de l'apprentissage adaptatif en ligne, nous proposerons une architecture pour le pilotage de ce type d'apprentissage adaptatif collaboratif. Cette solution est fondée sur des ontologies et orchestrée par un système multi-agents. Ce dernier est responsable du pilotage des parcours d'apprentissage, de la recommandation de parcours en mode collaboratif ou non par le biais d'une communication entre les différents agents intervenants et de la gestion des événements captés par le système.

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“…Thus, considering the interest in combining recommender systems and multi-agent systems for the present research work, a systematic literature review on multi-agent recommender systems [31] was developed. The literature review was conducted in two phases: in the first phase, the research focused on identifying literature reviews on multi-agent recommender systems, while in the second phase, we focused on research works that propose recommendation solutions based on agent technology and multi-agent systems.…”

Section: Multi-agent-based Recommender Systemsmentioning

confidence: 99%

Context-Based Multi-Agent Recommender System, Supported on IoT, for Guiding the Occupants of a Building in Case of a Fire

et al. 2022

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The evacuation of buildings in case of fire is a sensitive issue for civil society that also motivates the academic community to develop and study solutions to improve the efficiency of evacuating these spaces. The study of human behavior in fire emergencies has been one of the areas that have deserved the attention of researchers. However, this modeling of human behavior is difficult and complex because it depends on factors that are difficult to know and that vary from country to country. In this paper, a paradigm shift is proposed which, instead of focusing on modeling the behavior of occupants, focuses on conditioning this behavior by providing real-time information on the most efficient evacuation routes. Making this information available to occupants is possible with a solution that takes advantage of the growing use of the IoT (Internet of Things) in buildings to help occupants adapt to the environment. Supported by the IoT, multi-agent recommender systems can help users to adapt to the environment and provide the occupants with the most efficient evacuation routes. This paradigm shift is achieved through a context-based multi-agent recommender system based on contextual data obtained from IoT devices, which recommends the most efficient evacuation routes at any given time. The obtained results suggest that the proposed solution can improve the efficiency of evacuating buildings in the event of a fire; for a scenario with two hundred people following the system recommendations, the time they take to reach a safe place decreases by 17.7%.

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Multi-Agent-Based Recommender Systems: A Literature Review

Cited by 5 publications

References 33 publications

Multi-Agent-Based Film Editing Collaboration System

Multi-Agent-Based Film Editing Collaboration System

Towards a semantic platform for adaptive and collaborative e-learning

Context-Based Multi-Agent Recommender System, Supported on IoT, for Guiding the Occupants of a Building in Case of a Fire

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