Recommendation System as a User-Oriented Service for the Remote and Virtual Labs Selecting

Parkhomenko, Anzhelika; Gladkova, Olga

doi:10.1007/978-3-030-11935-5_57

Cited by 3 publications

(3 citation statements)

References 13 publications

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“…Each of them has its own functionality, advantages and disadvantages. The choosing of appropriate learning resources is quite complicated and requires consideration of many criteria, so it is important to use recommendation systems, in particular for selection the appropriate RL and VL [16] as well as the IoT platforms [7].…”

Section: Figure 1: Classification Of Online Engineering Technologiesmentioning

confidence: 99%

Virtual Environment for Internet of Things Technologies Studying

Yaremchenko¹,

Parkhomenko²,

Tulenkov³

et al. 2022

Computer Modeling and Intelligent Systems

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Section: Figure 1: Classification Of Online Engineering Technologiesmentioning

confidence: 99%

Virtual Environment for Internet of Things Technologies Studying

Yaremchenko¹,

Parkhomenko²,

Tulenkov³

et al. 2022

Computer Modeling and Intelligent Systems

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“…Other recent studies analyze the pedagogical scenario and how students assimilate notions and knowledge disseminated by laboratories provided over distance [33]- [35], as well as the students' acceptance and intention to use NTLs [36], [37]. Also, the work of Parkhomenko, Gladkova and Parkhomenko [38] rigorously classifies and characterizes didactic and pedagogic scenario of different NTLs. The authors provide a recommendation system aimed at helping teachers, students, and developers of new labs with information about features and possibilities of NTLs, considering the users the labs are aimed at, and the curricula they cope with.…”

Section: Didactical Aspectsmentioning

confidence: 99%

Non-Traditional Labs and Lab Network Initiatives: A Review

et al. 2021

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Lab-based education has always played an important role in teaching students. Making remote and virtual labs communicate with one another by creating networks of labs can enhance the traditional way of learning as well as reduce the costs of implementing and using labs. This paper provides a review of the literature on non-traditional labs and lab network initiatives up to 2020. With the term ‘non-traditional labs’, we mean virtual, remote and hybrid labs, whereas with the term ‘lab network’, we indicate a set of two or more cooperating labs typically connected through the internet. In this study, we used a recent and comprehensive framework for data collection, organization and analysis to gather information on 40 non-traditional labs and lab network initiatives. Thanks to this framework, the outcomes of our work highlight interesting trends of lab-based education, which pertain to didactical, organizational and technical aspects.

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“…It is becoming hardly possible to accurately obtain user interest preferences directly from tags (Indra and Thangaraj, 2019). (2) Recommendation service is a user-oriented service that starts with user needs and ends with satisfying user needs, and inefficient recommendation services will affect user satisfaction (Parkhomenko et al , 2019). However, most existing social tagging recommendations confuse the offline optimization process and online service process, which results in too long online time for users and affects the efficiency of online recommendation.…”

Section: Introductionmentioning

confidence: 99%

Topic optimization–incorporated collaborative recommendation for social tagging

Pan

Zeng

Ling

2022

DTA

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PurposeWith the continuous increase of users, resources and tags, social tagging systems gradually present the characteristics of “big data” such as large number, fast growth, complexity and unreliable quality, which greatly increases the complexity of recommendation. The contradiction between the efficiency and effectiveness of recommendation service in social tagging is increasingly becoming prominent. The purpose of this study is to incorporate topic optimization into collaborative filtering to enhance both the effectiveness and the efficiency of personalized recommendations for social tagging.Design/methodology/approachCombining the idea of optimization before service, this paper presents an approach that incorporates topic optimization into collaborative recommendations for social tagging. In the proposed approach, the recommendation process is divided into two phases of offline topic optimization and online recommendation service to achieve high-quality and efficient personalized recommendation services. In the offline phase, the tags' topic model is constructed and then used to optimize the latent preference of users and the latent affiliation of resources on topics.FindingsExperimental evaluation shows that the proposed approach improves both precision and recall of recommendations, as well as enhances the efficiency of online recommendations compared with the three baseline approaches. The proposed topic optimization–incorporated collaborative recommendation approach can achieve the improvement of both effectiveness and efficiency for the recommendation in social tagging.Originality/valueWith the support of the proposed approach, personalized recommendation in social tagging with high quality and efficiency can be achieved.

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Recommendation System as a User-Oriented Service for the Remote and Virtual Labs Selecting

Cited by 3 publications

References 13 publications

Virtual Environment for Internet of Things Technologies Studying

Virtual Environment for Internet of Things Technologies Studying

Non-Traditional Labs and Lab Network Initiatives: A Review

Topic optimization–incorporated collaborative recommendation for social tagging

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