A Comparison of Similarity Measures for Neighbourhood Based Collaborative Filtering Recommender Systems

Beniwal, Rohit; Khairwal, Rahul; Mahajan, Ritvik; Singh, Sarthak Pratap Narayan

doi:10.1109/asiancon51346.2021.9544901

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…As online learning and education continue to evolve, some researchers are beginning to apply the recommendation system method to the field of education and to improving the algorithm to achieve a greater recommendation effect. The collaborative filtering-based course recommendation algorithm can be divided into the recommendation method based on the neighborhood (Beniwal et al, 2021;Kużelewska, 2020) and the model-based method (Zarzour et al, 2020). The recommendation method based on neighborhood relies on the calculation of the similarity of users or items to recommend the relevant courses of adjacent users.…”

Section: Traditional Course Recommendation Algorithmmentioning

confidence: 99%

Personalized Education Resource Recommendation Method Based on Deep Learning in Intelligent Educational Robot Environments

Yang

2023

International Journal of Information Technologies and Systems Approach

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The goal of this article is to analyze the problem of low computational efficiency and propagation error rate in entity recognition and relation extraction. This paper proposes a personalized education resource recommendation algorithm framework XMAMBLSTM based on deep learning in an intelligent education robot environment. XMAMBLSTM uses XLNet to assign word vectors to text sequences, employs a Multi-Bi-LSTM layer to represent complex information of word vectors, and combines a multi-headed attention layer to realize weight distribution of each word vector. The experimental results show that compared with the traditional collaborative filtering algorithm, the comprehensive evaluation indexes of the proposed method, based on the intelligent education robot environment on the two platforms, are higher than 5.05% and 17.3%, respectively.

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Section: Traditional Course Recommendation Algorithmmentioning

confidence: 99%

Personalized Education Resource Recommendation Method Based on Deep Learning in Intelligent Educational Robot Environments

Yang

2023

International Journal of Information Technologies and Systems Approach

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Collaborative Filtering-Based Personalized Recommendations: Challenges, Limitations, and Applications

Nguyen,

Le,

et al. 2024

Lecture Notes on Data Engineering and Communications Technologies

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A Comparison of Similarity Measures for Neighbourhood Based Collaborative Filtering Recommender Systems

Cited by 2 publications

References 13 publications

Personalized Education Resource Recommendation Method Based on Deep Learning in Intelligent Educational Robot Environments

Personalized Education Resource Recommendation Method Based on Deep Learning in Intelligent Educational Robot Environments

Collaborative Filtering-Based Personalized Recommendations: Challenges, Limitations, and Applications

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