ERSDO: E-learning Recommender System based on Dynamic Ontology

Amane, Meryem; Aissaoui, Karima; Berrada, Mohammed

doi:10.1007/s10639-022-10914-y

Cited by 15 publications

(7 citation statements)

References 16 publications

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“…By merging numerous recommendations approaches, hybrid recommender systems are essential to improving the performance of recommendation systems [25,26,27]. Hybridization's primary objective is to overcome the shortcomings of each individual recommender system and enhance the system as a whole [28,29,30,31]. The literature has a variety of hybridization techniques, including weighted, switching, mixed, feature combination, cascade, feature improvement, and meta-level approaches [32].…”

Section: Hybrid Recommender Systemsmentioning

confidence: 99%

OntoCommerce: Incorporating Ontology and Sequential Pattern Mining for Personalized E-Commerce Recommendations

Mustafa,

Jhamat,

Arshad

et al. 2024

IEEE Access

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The abundance of information on online purchasing websites makes it challenging for customers to locate products that match their preferences. However, the cold-start problem arises when there isn't enough previous data, making it harder to make accurate recommendations for new customers or products. The enormous number of possible customers and products in a recommendation system leads to sparse data, which makes it harder to generate relevant recommendations and causes the sparsity problem. In addition, existing e-commerce recommender systems have difficulty making accurate product recommendations because they disregard individual consumer characteristics. In order to overcome these limitations, a hybrid recommender system combining ontology and sequential pattern mining (SPM) techniques is proposed. The strategy entails constructing an ontology that encompasses customer and product-related knowledge in the e-commerce domain. This ontology is then utilized to calculate customer preference similarities and generate predictions for the intended customer. The SPM algorithm is applied to the results of collaborative filtering to generate personalized recommendations for the customer. Experiments have demonstrated that the hybrid recommender system outperforms existing methods and resolves the cold-start problem and data sparsity in e-commerce recommender systems effectively. Even with limited initial data, the system generates accurate and individualized recommendations based on ontological domain knowledge and the customer's sequential purchase patterns. By integrating ontology and sequential pattern mining, this strategy enhances the precision and individualization of the e-commerce industry's recommendation process.

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Section: Hybrid Recommender Systemsmentioning

confidence: 99%

OntoCommerce: Incorporating Ontology and Sequential Pattern Mining for Personalized E-Commerce Recommendations

Mustafa,

Jhamat,

Arshad

et al. 2024

IEEE Access

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“…M. Amane et al, [45] presented a dynamic ontology-based EL RecSys. In order to get the top-k recommendations This article has been accepted for publication in IEEE Access.…”

Section: Literature Reviewmentioning

confidence: 99%

Developing a Personalized E-Learning and MOOC Recommender System in IoT-Enabled Smart Education

Amin,

Uddin,

Mashwani

et al. 2023

IEEE Access

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Smart strategies and intelligent technologies are enabling the designing of a smart learning environment that successfully supports the development of personalized learning and adaptive learning. This trend towards integration is in line with the growing prevalence of Internet of Things (IoT)-enabled smart education systems, which can leverage Machine Learning (ML) techniques to provide Personalized Course Recommendations (PCR) to students. Furthermore, the existing recommendation techniques are based on either explicit or implicit feedback and fail to capture the changes in learners' preferences while integrating implicit or explicit feedback. To this end, this paper proposes a new model for personalized learning and PCR that is enabled by a smart E-Learning (EL) platform. The model aims to gather data on students' academic performance, interests, and learning preferences and utilize this data to recommend the courses that will be most beneficial to each student. The proposed approach makes suggestions based on the learner's interactions with the system and the cosine similarity in related contents by combining explicit (user ratings) and implicit (views and behavior) methodologies. The suggested method makes use of ML algorithms and an EL Recommender System (RecSys) based on Collaborative Filtering (CF).This includes Random Forest Regressor (RFR), Decision Tree Regressor (DTR), K-Nearest Neighbors (KNN), Singular Value Decomposition (SVD), eXtreme Gradient Boosting Regressor (XGBR), and Linear Regression (LR). The proposed solution is benchmarked against existing approaches on both predictive accuracy and running time. Experimental results are conducted based on two benchmark datasets (Coursera and Udemy). The proposed model outperforms existing top-K recommendations techniques in terms of accuracy metrics such as precision@k, Mean Average Precision (MAP)@k, recall@k, Normalized Discounted Cumulative Gain (NDCG)@k, Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE) for PCR. From the experiments, it can be shown that SVD can perform well in terms of higher accuracy and MAP and NDCG and lower MAE, RMSE, and MSE values when contrasted to other proposed algorithms because it is better suited to capture complex student-course interactions. The proposed solutions are promising on two different datasets and can be applied to various RecSys domains.

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“…The conceptual model of ontology permits reasoning at all concept levels. Hence, an ontologybased RS is an approach of knowledge-based RS techniques that is very popular in the e-learning domain due to its capability to cluster the learners' models based on their educational background, learning style, study trajectory, and knowledge level (Amane et al, 2022;Tarus et al, 2018). In addition, it resolves the cold-start problem (Jeevamol & Renumol, 2021).…”

Section: Ontology-based Rssmentioning

confidence: 99%

A comparative analysis of different recommender systems for university major and career domain guidance

et al. 2022

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Academic advising is inhibited at most of the high schools to help students identify appropriate academic pathways. The choice of a career domain is significantly influenced by the complexity of life and the volatility of the labor market. Thus, high school students feel confused during the shift period from high school to university, especially with the enormous amounts of data available on the Web. In this paper, an extensive comparative study is conducted to investigate five approaches of recommender systems for university study field and career domain guidance. A novel ontology is constructed to include all the needed information for this purpose. The developed approaches considered user-based and item-based collaborative filtering, demographic-based recommendation, knowledge base supported by case-based reasoning, ontology, as well as different hybridizations of them. A case study on Lebanese high school students is analyzed to evaluate the effectiveness and efficiency of the implemented approaches. The experimental results indicate that the knowledge-based hybrid recommender system, combined with the user-based collaborative filtering and braced with case-based reasoning as well as ontology, generated 98% of similar cases, 95% of them are personalized based on the interests of the high school students. The average usefulness feedback and satisfaction level of the students concerning this proposed hybrid approach reached 95% and 92.5% respectively, which could be a solution to similar problems, regardless of the application domain. Besides, the constructed ontology could be reused in other systems in the educational domain.

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ERSDO: E-learning Recommender System based on Dynamic Ontology

Cited by 15 publications

References 16 publications

OntoCommerce: Incorporating Ontology and Sequential Pattern Mining for Personalized E-Commerce Recommendations

OntoCommerce: Incorporating Ontology and Sequential Pattern Mining for Personalized E-Commerce Recommendations

Developing a Personalized E-Learning and MOOC Recommender System in IoT-Enabled Smart Education

A comparative analysis of different recommender systems for university major and career domain guidance

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