Dynamic Neighbourhood Identification Based on Multi-clustering in Collaborative Filtering Recommender Systems

Kużelewska, Urszula

doi:10.1007/978-3-030-48256-5_40

Cited by 7 publications

(7 citation statements)

References 13 publications

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“…An example application is [22], a genetic-based recommender system, in which the neighbourhood was hybridized with the latent factor models. Experiments with this technique and M − CCF are described in [23].…”

Section: Background and Related Workmentioning

confidence: 99%

Selection of clusters based on internal indices in multi-clustering collaborative filtering recommender system

Kużelewska

2024

International Journal of Electronics and Telecommunications

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The successful application of a multi-clusteringbased neighborhood approach to recommender systems has led to increased recommendation accuracy and the elimination of divergence related to differences in clustering methods traditionally used. The Multi-Clustering Collaborative Filtering algorithm was developed to achieve this, as described in the author’s previous papers. However, utilizing multiple clusters poses challenges regarding memory consumption and scalability. Not all partitionings are equally advantageous, making selecting clusters for the recommender system’s input crucial without compromising recommendation accuracy. This article presents a solution for selecting clustering schemes based on internal indices evaluation. This method can be employed for preparing input data in collaborative filtering recommender systems. The study’s results confirm the positive impact of scheme selection on the overall recommendation performance, as it typically improves after the selection process. Furthermore, a smaller number of clustering schemes used as input for the recommender system enhances scalability and reduces memory consumption. The findings are compared with baseline recommenders’ outcomes to validate the effectiveness of the proposed approach.

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Section: Background and Related Workmentioning

confidence: 99%

Selection of clusters based on internal indices in multi-clustering collaborative filtering recommender system

Kużelewska

2024

International Journal of Electronics and Telecommunications

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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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“…An example application is [21], a genetic-based recommender system, in which the neighbourhood was hybridized with the latent factor models. Experiments with this technique and M − CCF are described in [22].…”

Section: Background and Related Workmentioning

confidence: 99%

“…The proposed method performs on several types of clustering schemes that are delivered for M − CCF algorithm's input. It is implemented in the following way (for the original version, with one type of a clustering scheme, check in [18], [22]).…”

Section: Presentation Of M-ccf Algorithmmentioning

confidence: 99%

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Scheme Selection based on Clusters' Quality in Multi-Clustering M-CCF Recommender System

Kużelewska

2022

Preprint

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Identification of a neighbourhood that is based on multi-clusters was successfully applied to recommender systems, increasing recommendation accuracy and eliminating divergence related to differences in clustering schemes generated by traditional methods. Multi-Clustering Collaborative Filtering algorithm was developed for this purpose that was described in the author's previous papers. However, the solutions involving many clusters face substantial challenges around memory consumption and scalability. Differently, they do not equally take advantage of all the partitionings. Selection of the clusters to forward to the recommender system's input, without deterioration in recommendation accuracy, can be used as a precaution to address these problems. The article describes a solution of a clustering schemes' selection based on internal indices evaluation, that can be applied for input data preparation in collaborative filtering recommender systems. The results reported in this paper confirmed its positive impact on the system's overall recommendation performance, which usually increases after the selection of schemes. Additionally, a smaller number of clustering schemes on an input of a recommender system improves its scalability including memory consumption. The obtained values were compared with baseline recommenders' outcomes.

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Dynamic Neighbourhood Identification Based on Multi-clustering in Collaborative Filtering Recommender Systems

Cited by 7 publications

References 13 publications

Selection of clusters based on internal indices in multi-clustering collaborative filtering recommender system

Selection of clusters based on internal indices in multi-clustering collaborative filtering recommender system

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

Scheme Selection based on Clusters' Quality in Multi-Clustering M-CCF Recommender System

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