Weighting based approach for learning resources recommendations

Bourkoukou, Outmane; Achbarou, Omar

doi:10.30630/joiv.2.3.124

Cited by 7 publications

(8 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The aim is to create a concept map for adaptive learning to provide an educational advisor for students. Alinani et al (2016) proposed a heterogeneous educational resource recommender system based on user preferences Bourkoukou and Achbarou (2018) aimed to build a personal recommender system that results in useful content and better recommendations in the shortest possible time [28]. The proposed system is a web-based client-side application that uses user pro les to form neighborhoods and calculates predictions using weights.…”

Section: Development Of Traditional Methodsmentioning

confidence: 99%

Dynamic Educational Recommender System Based on Improved LSTM Neural Network

Yazdi

Mahdavi

Kheirabadi

2022

Preprint

View full text Add to dashboard Cite

Virtual learning environments have become widespread in today's society to avoid time and space constraints and to share high-quality learning resources. In the process of human-computer interaction, student behaviors are recorded instantly. This article aims to design an educational recommendation system according to the individual's interests in educational resources, which is evaluated based on clicking or downloading the source and the score given to that source by the user. In online tutorials, in addition to the problem of choosing the right source, we face the challenge of how to be aware of diversity in users' preferences and tastes, especially their short-term interests in the near future, at the beginning of a session. We assume that the user's interests consist of two parts: 1) the user's long-term interests, which include the user's constant interests based on the history of the user's dynamic activities, and 2) the user's short-term interests, which indicate the user's current interests. Due to the use of Bilstm networks and their gradual learning feature, the proposed model supports learners' behavioral changes, with an average accuracy of 0.9978 and an error of 0.0051 offers more appropriate recommendations than similar articles.

show abstract

Section: Development Of Traditional Methodsmentioning

confidence: 99%

Dynamic Educational Recommender System Based on Improved LSTM Neural Network

Yazdi

Mahdavi

Kheirabadi

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The objective of adaptive access is to simplify the time/energy consumed for unnecessary navigation. This enables a better and user-friendly system which adds to the pre-existing merits of the digital library (Bourkoukou and Achbarou, 2018). The objective of the proposed model is to not only provide a learning environment with a personalized recommendation system but also address sparsity and scalability issues.…”

Section: Personal Learning Environmentmentioning

confidence: 99%

Towards personal learning environment by enhancing adaptive access to digital library using ontology-supported collaborative filtering

Kumaran

Latha

2023

LHT

View full text Add to dashboard Cite

PurposeThe purpose of this paper is to provide adaptive access to learning resources in the digital library.Design/methodology/approachA novel method using ontology-based multi-attribute collaborative filtering is proposed. Digital libraries are those which are fully automated and all resources are in digital form and access to the information available is provided to a remote user as well as a conventional user electronically. To satisfy users' information needs, a humongous amount of newly created information is published electronically in digital libraries. While search applications are improving, it is still difficult for the majority of users to find relevant information. For better service, the framework should also be able to adapt queries to search domains and target learners.FindingsThis paper improves the accuracy and efficiency of predicting and recommending personalized learning resources in digital libraries. To facilitate a personalized digital learning environment, the authors propose a novel method using ontology-supported collaborative filtering (CF) recommendation system. The objective is to provide adaptive access to learning resources in the digital library. The proposed model is based on user-based CF which suggests learning resources for students based on their course registration, preferences for topics and digital libraries. Using ontological framework knowledge for semantic similarity and considering multiple attributes apart from learners' preferences for the learning resources improve the accuracy of the proposed model.Research limitations/implicationsThe results of this work majorly rely on the developed ontology. More experiments are to be conducted with other domain ontologies.Practical implicationsThe proposed approach is integrated into Nucleus, a Learning Management System (https://nucleus.amcspsgtech.in). The results are of interest to learners, academicians, researchers and developers of digital libraries. This work also provides insights into the ontology for e-learning to improve personalized learning environments.Originality/valueThis paper computes learner similarity and learning resources similarity based on ontological knowledge, feedback and ratings on the learning resources. The predictions for the target learner are calculated and top N learning resources are generated by the recommendation engine using CF.

show abstract

“…Untuk pembobotan pada dataset dilakukan pada penelitian Bourkoukou dan Achbarou [12] yakni menggunakan metode collaborative filtering dan model learning style. Penelitian ini menggunakan history dari akses user sebagai input learning dan melakukan pembobotan pada data learning.…”

Section: Tinjauan Literaturunclassified

“…Berdasarkan penelitian sebelumnya [6], [10]- [12], penelitian ini memiliki kesamaan dalam hal tema penelitian yakni sistem rekomendasi dengan menggunakan collaborative filtering. Secara lebih rinci, terdapat kesamaan objek penelitian, yakni anime, dengan penelitian yang dilakukan oleh Girsang dkk.…”

Section: Tinjauan Literaturunclassified

“…Pembeda algoritma pada model collaborative filtering yang dibangun juga ditemui pada penelitian [10] dimana pada penelitian tersebut digunakan algoritma KNN dan SlopeOne sedangkan pada penelitian ini model collaborative filtering yang dibangun menggunakan algoritma KNN serta menambahkan bobot dari status pada nilai rating. Selain itu, implementasi pembobotan dataset juga dilakukan pada penelitian [12], dimana pembobotan dilakukan pada data sebelum diproses selanjutnya untuk diprediksi. Perbedaan dengan penelitian ini terdapat pada model yang digunakan untuk pembobotan.…”

Section: Tinjauan Literaturunclassified

See 1 more Smart Citation

Improvement of KNN Collaborative Filtering Model in User-based Approach on Anime Recommendation System

Permadi,

Raharjo

2023

SISTEMASI

View full text Add to dashboard Cite

Penelitian ini bertujuan untuk mengatasi masalah pemberian rekomendasi yang sesuai dengan preferensi user. Untuk pengujian model, digunakan dataset MyAnimeList yang dapat diakses dari situs Kaggle. Hasil dari penelitian ini adalah rancangan sistem rekomendasi berdasarkan preferensi user lainnya (user-based model). Metode yang diusulkan menggunakan model collaborative filtering dengan menggunakan algoritma KNN dan penambahan bobot pada dataset. Data yang digunakan pada penelitian ini berjumlah 193.272 data yang berisi rating user terhadap anime, dan terdiri dari atribut: username, anime_id, my_score, dan my_status. Sebagai pengembangan model KNN collaborative filtering, dilakukan pembobotan pada nilai rating berdasarkan status dari user. Penentuan nilai pembobotan didasarkan oleh kuisioner yang telah diisi oleh 105 responden. Selain pembobotan, nilai my_score dan my_status akan digabungkan dan dinormalisasi menggunakan MinMaxNormalization. Berbagai nilai parameter 𝑘 digunakan pada algoritma KNN di penelitian ini, yaitu: 3, 5, 9, 15, 23, 33, dan 45. Variasi parameter digunakan untuk mencari nilai 𝑘 terbaik untuk diimplementasikan pada KNN yang menggunakan matriks pearson similarity dalam mencari nilai similaritas antar user. Hasil pengujian menunjukkan bahwa nilai Mean Absolute Error terbaik adalah 0,14726 dan nilai Root Mean Square Error terbaik sebesar 0,19855, pada parameter 𝑘 = 5. Hasil dari model modifikasi ini juga menunjukkan bahwa KNN collaborative filtering yang ditambahkan pembobotan dapat memprediksi rating dengan nilai error yang stabil dan relatif rendah pada semua nilai 𝑘.

show abstract

Weighting based approach for learning resources recommendations

Cited by 7 publications

References 16 publications

Dynamic Educational Recommender System Based on Improved LSTM Neural Network

Dynamic Educational Recommender System Based on Improved LSTM Neural Network

Towards personal learning environment by enhancing adaptive access to digital library using ontology-supported collaborative filtering

Improvement of KNN Collaborative Filtering Model in User-based Approach on Anime Recommendation System

Contact Info

Product

Resources

About