Autonomous recommender system architecture for virtual learning environments

Monsalve-Pulido, Julián Alberto; Aguilar, José; Montoya, Edwin; Salazar, Camilo

doi:10.1016/j.aci.2020.03.001

Cited by 20 publications

(10 citation statements)

References 37 publications

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“…The number of times a term was detected as a keyword in the 493 papers examined is revealed using keyword co-occurrence analysis. [22]. We conducted a bibliometric review and synthesis of the research that developed and deployed LSTM learning models during the previous five years.…”

Section: Authorship and Publication Mappingmentioning

confidence: 99%

A VOS analysis of LSTM Learners Classification for Recommendation System

Pasha¹,

Rao²,

Geetha³

et al. 2023

IJRITCC

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In response to the growing popularity of social web apps, much research has gone into analyzing and developing an AI-based responsive suggestion system. Machine learning and neural networks come in many forms that help online students choose the best texts for their studies. However, when training recommendation models to deal with massive amounts of data, traditional machine learning approaches require additional training models. As a result, they are deemed inappropriate for the personalized recommender generation of learning systems. In this paper, we examine LSTM-based strategies in order to make useful recommendations for future research.

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Section: Authorship and Publication Mappingmentioning

confidence: 99%

A VOS analysis of LSTM Learners Classification for Recommendation System

Pasha¹,

Rao²,

Geetha³

et al. 2023

IJRITCC

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“…and/or the resources to be recommended (diseases, symptoms, treatments, among many others). Based on these needs, it is necessary to specify a knowledge‐based RS, called Intelligent Recommender System (Aguilar et al, 2017; Monsalve‐Pulido et al, 2020; Ricci et al, 2011), which considers the mechanisms of knowledge representation and reasoning engines, among other aspects.…”

Section: Architecture Of Our Hybrid Recommender Systemmentioning

confidence: 99%

“…Other fundamental aspects are the flexibility of the proposed architectures to incorporate other reasoning methods (e.g. fuzzy using paradigms such as fuzzy cognitive maps (Aguilar, 2001)), feature engineering processes (Pacheco et al, 2014), or migrate the architecture to an autonomic scheme (Monsalve-Pulido et al, 2020). Our architectural proposal can be easily extended with these characteristics.…”

Section: Analysis Of the Poq Inputmentioning

confidence: 99%

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A hybrid recommender system based on description/dialetheic logic and linked data

Santos

Aguilar

2022

Expert Systems

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In this research, we developed a hybrid recommender system based on description/dialetheic logic, which provides an innovative architecture for the recommendation based on ambiguous reasoning. In addition, our approach allows enriching the knowledge during the reasoning using the linked data paradigm. Thus, the architecture allows the integration of linked data, in order to be exploited by the recommender. On the other hand, the reasoning mechanisms of dialetheic logic allow handling situations of contradiction or inconsistency, to determine the information to offer to users. According to the reviewed literature, our proposal is the first that implements a dialetheic engine in a Recommender System. In general, there are a lot of works about the utilization of linked open data (LOD) to improve the Recommender Systems, for example, to enrich the information to recommend or to solve the cold start problem. However, there are no proposals to deal with the problems of ambiguity, incoherence or incompleteness of the information in these environments extended with LOD. In this way, this paper proposes a hybrid reasoning engine that uses the linked data paradigm for the knowledge extraction, and dialetheic logic for the management of inconsistency/ambiguity information, in order to obtain recommendations. Particularly, the information extracted with Linked Data is processed with the Dialetheic Logic reasoner to solve ambiguous cases. Thus, each recommendation is enriched with related content extracted from the linked data sources, which has been disambiguated. The results are very promising since our hybrid reasoning mechanism allows obtaining more precise recommendations, considering the different classical states of ambiguity in dialetheic logic (contingent statements about the future, failure of a presupposition, vagueness, counterfactual reasoning), according to various metrics of quality used to evaluate the recommendations achieved.

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“…Numerous business cases and applications may require strict full local (private) control over the data and infrastructure [11][12][13][14], yet may pose substantial computing demands, e.g. for document management [15,16], image processing [17,18], surveillance applications [19], education system management [20,21] and process simulation [22]. This is where the private cloud can come into play for both IaaS and PaaS.…”

Section: Introductionmentioning

confidence: 99%

Design of a small-scale and failure-resistant IaaS cloud using OpenStack

Heuchert

Rimal

Reisslein

et al. 2021

ACI

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PurposeMajor public cloud providers, such as AWS, Azure or Google, offer seamless experiences for infrastructure as a service (IaaS), platform as a service (PaaS) and software as a service (SaaS). With the emergence of the public cloud's vast usage, administrators must be able to have a reliable method to provide the seamless experience that a public cloud offers on a smaller scale, such as a private cloud. When a smaller deployment or a private cloud is needed, OpenStack can meet the goals without increasing cost or sacrificing data control.Design/methodology/approachTo demonstrate these enablement goals of resiliency and elasticity in IaaS and PaaS, the authors design a private distributed system cloud platform using OpenStack and its core services of Nova, Swift, Cinder, Neutron, Keystone, Horizon and Glance on a five-node deployment.FindingsThrough the demonstration of dynamically adding an IaaS node, pushing the deployment to its physical and logical limits, and eventually crashing the deployment, this paper shows how the PackStack utility facilitates the provisioning of an elastic and resilient OpenStack-based IaaS platform that can be used in production if the deployment is kept within designated boundaries.Originality/valueThe authors adopt the multinode-capable PackStack utility in favor of an all-in-one OpenStack build for a true demonstration of resiliency, elasticity and scalability in a small-scale IaaS. An all-in-one deployment is generally used for proof-of-concept deployments and is not easily scaled in production across multiple nodes. The authors demonstrate that combining PackStack with the multi-node design is suitable for smaller-scale production IaaS and PaaS deployments.

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Autonomous recommender system architecture for virtual learning environments

Cited by 20 publications

References 37 publications

A VOS analysis of LSTM Learners Classification for Recommendation System

A VOS analysis of LSTM Learners Classification for Recommendation System

A hybrid recommender system based on description/dialetheic logic and linked data

Design of a small-scale and failure-resistant IaaS cloud using OpenStack

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