An ontology-based hybrid e-learning content recommender system for alleviating the cold-start problem

Joy, Jeevamol; Renumol, V. G.

doi:10.1007/s10639-021-10508-0

Cited by 39 publications

(26 citation statements)

References 81 publications

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“…E‐learning recommendation architecture (ELRA) has improved the trainer mentoring by the text mining and strengthened the recommendation process. Jeevamol et al 25 proposed the effective E‐learning recommender system for the generation of personalized recommendation. An ontology based content recommendation system was developed in this research to address the cold‐start issues.…”

Section: Related Workmentioning

confidence: 99%

An efficient framework for the similarity prediction with query recommendation in E‐learning system

Prasad

Muthappa²

2022

Concurrency and Computation

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Summary A novel model of data similarity estimation and clustering method is proposed in this article to retrieve the relevant data with the best matching in big data processing. An advanced model of graph distance pattern (GDP) method with lexical subgroup (LS) system is used to estimate the similarity between the query data and the entire database. With the help of neural network, the relevancy of feature attributes in the database are predicted and matching index is sorted to provide the recommended data for given query data. This was achieved by using the correlated sim‐neural network (CSNN). This is an enhanced model of neural network technology to find the relevancy based on the correlation factor of feature set. The training process of CSNN classifier is carried by estimating the correlation factor of the attributes of dataset. These are forms as the clusters and paged with proper indexing based on the LS parameter of similarity metric. The results obtained by the proposed system for recall, precision, accuracy, error rate, F‐measure, kappa coefficient, specificity, and MCC are 0.98, 0.98, 0.97, 0.03, 0.99, 0.991, 0.986, and 0.984, respectively.

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Section: Related Workmentioning

confidence: 99%

An efficient framework for the similarity prediction with query recommendation in E‐learning system

Prasad

Muthappa²

2022

Concurrency and Computation

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“…In order to more accurately recommend academic resources, it is necessary to obtain and describe the characteristics of academic users' interest in academic resources, so as to depict accurate academic user portraits. All the interest characteristics of users can be divided into different types [11]. One classification method is to divide user portraits into explicit features and implicit features according to different acquisition methods.…”

Section: B Academic User Portraitmentioning

confidence: 99%

Research on Personalized Recommendation of High-Quality Academic Resources based on user Portrait

Xu¹,

Man²,

Sabri³

et al. 2022

IJACSA

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With the advent of the era of big data, the phenomenon of information overload is becoming increasingly serious. It is difficult for academic users to obtain the information they want quickly and accurately in the face of massive academic resources. Aiming at the optimization of academic resource recommendation services, this paper constructs a multidimensional academic user portrait model and proposes an Academic Resource Recommendation Algorithm Based on user portrait. This paper first, combs the relevant literature and information; Secondly, to obtain the attribute tags of multidimensional user portraits, a set of questionnaires are designed to collect the real information of academic users, and the corresponding academic user portrait model is constructed; Then, the collected data is processed through certain rules, and the user is quantitatively modeled based on the data through mathematical means; Finally, through the construction of the completed academic user portrait model, combined with collaborative filtering algorithm, provide personalized academic resource recommendation services for academic users. Through the verification and analysis of simulation experiments, the Academic Resource Recommendation Algorithm Based on the user portrait proposed in this paper plays a great role in expanding users' interest fields and discovering new hobbies across fields and disciplines.

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“…However, recent reviews show the growing significance of personalisation and recommendation systems in e-learning models, and ontologies are proven to be useful in this respect [17]. Jando et al [22] show that most techniques use such an ontology to accomplish personalisation, such as the work in [18,23]. A review by Tarus et al [31] presents the state-of-the-art for "ontology-based recommenders in e-learning".…”

Section: Related Workmentioning

confidence: 99%

EduCOR: An Educational and Career-Oriented Recommendation Ontology

Ilkou

Abu-Rasheed

Tavakoli

et al. 2021

Lecture Notes in Computer Science

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With the increased dependence on online learning platforms and educational resource repositories, a unified representation of digital learning resources becomes essential to support a dynamic and multi-source learning experience. We introduce the EduCOR ontology, an educational, career-oriented ontology that provides a foundation for representing online learning resources for personalised learning systems. The ontology is designed to enable learning material repositories to offer learning path recommendations, which correspond to the user’s learning goals and preferences, academic and psychological parameters, and labour-market skills. We present the multiple patterns that compose the EduCOR ontology, highlighting its cross-domain applicability and integrability with other ontologies. A demonstration of the proposed ontology on the real-life learning platform eDoer is discussed as a use case. We evaluate the EduCOR ontology using both gold standard and task-based approaches. The comparison of EduCOR to three gold schemata, and its application in two use-cases, shows its coverage and adaptability to multiple OER repositories, which allows generating user-centric and labour-market oriented recommendations.Resource: https://tibonto.github.io/educor/.

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An ontology-based hybrid e-learning content recommender system for alleviating the cold-start problem

Cited by 39 publications

References 81 publications

An efficient framework for the similarity prediction with query recommendation in E‐learning system

An efficient framework for the similarity prediction with query recommendation in E‐learning system

Research on Personalized Recommendation of High-Quality Academic Resources based on user Portrait

EduCOR: An Educational and Career-Oriented Recommendation Ontology

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