A Comparative Study Between K-Nearest Neighbors and K-Means Clustering Techniques of Collaborative Filtering in e-Learning Environment

Zriaa, Rajae; Amali, Said

doi:10.1007/978-3-030-66840-2_21

Cited by 8 publications

(6 citation statements)

References 15 publications

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“…Collaborative filtering recommends items based on the ratings of similar users. K-Nearest Neighbors (KNN) [13,14] is employed for this purpose, utilizing a similarity metric to identify the most similar users. In addition to traditional collaborative filtering, the system also incorporates an item-based algorithm that analyses users' behaviour records to calculate item similarities.…”

Section: Collaborative Filteringmentioning

confidence: 99%

Enhancing Online Learning

Yamaguchi

2024

Preprint

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In the contemporary digital landscape, online learning has emerged as a widely embraced mode of education due to its accessibility and cost-effectiveness. Timely delivery of courses tailored to learners’ needs is pivotal in maintaining their focus and commitment to learning. This project highlights the significant role of recommendation systems in attracting learners to focus on their learning paths and achieve their goals. It underscores the economic accessibility and the opportunities for individuals to engage with essential career skills. These concerns are particularly salient given the high cost of traditional education and the perceived elitism of certain institutions, which create barriers for economically disadvantaged learners. The project employs a multi-faceted approach to enhance recommendation accuracy. Initially, it collects user information to gauge Feature Ratings, reflecting learners’ reactions to courses. Additionally, course descriptions undergo analysis to ascertain word importance and inter-course relevance. While a standalone content-based approach proves insufficient, the project adopts a collaborative filtering approach next. Here, Feature Ratings are learned through a K-Nearest Neighbors (KNN) model, leveraging a similarity metric to identify similar learners. Crucially, the project integrates these methodologies into a Hybrid Filtering model, combining the strengths of both approaches for optimal performance. Performance evaluation showcases the system’s efficacy, measured through Hit Rate and F1 Score accuracies, demonstrating its effectiveness in enhancing learning outcomes.

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Section: Collaborative Filteringmentioning

confidence: 99%

Enhancing Online Learning

Yamaguchi

2024

Preprint

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“…Zriaa and Amali [28] compared KNN with "k-means clustering" to determine the most efficient method of prediction in an e-learning recommender system. The most commonly employed technique for evaluating the efficiency of an algorithm's performance in terms of accuracy is mean absolute error (MAE).…”

Section: Related Workmentioning

confidence: 99%

Personalized E-Learning Recommender System Based on Autoencoders

El Youbi El Idrissi,

Akharraz,

Ahaitouf

2023

ASI

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Through the Internet, learners can access available information on e-learning platforms to facilitate their studies or to acquire new skills. However, finding the right information for their specific needs among the numerous available choices is a tedious task due to information overload. Recommender systems are a good solution to personalize e-learning by proposing useful and relevant information adapted to each learner using a set of techniques and algorithms. Collaborative filtering (CF) is one of the techniques widely used in such systems. However, the high dimensions and sparsity of the data are major problems. Since the concept of deep learning has grown in popularity, various studies have emerged to improve this form of filtering. In this work, we used an autoencoder, which is a powerful model in data dimension reduction, feature extraction and data reconstruction, to learn and predict student preferences in an e-learning recommendation system based on collaborative filtering. Experimental results obtained using the database created by Kulkarni et al. show that this model is more accurate and outperforms models based on K-nearest neighbor (KNN), singular value decomposition (SVD), singular value decomposition plus plus (SVD++) and non-negative matrix factorization (NMF) in terms of the root-mean-square error (RMSE) and mean absolute error (MAE).

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“…The K-means algorithm is well known for its efficiency and power in clustering a large data set compared to other knearest neighbors" algorithm [40]. Moreover, it is considered one of the most popular clustering algorithms for unsupervised learning.…”

Section: ) K-means Clusteringmentioning

confidence: 99%

Trace Learners Clustering to Improve Learning Object Recommendation in Online Education Platforms

Rajae,

Said,

Nour-eddine

2022

IJACSA

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E-learning platformspropose pedagogical pathways where learners are invited to mobilize their autonomy to achieve the learning objectives. However, some learners face a set of cognitive barriers that require additional learning objects to progress in the course. A mediating recommendation system is one of the efficient solutions to reinforce the resilience of online platforms, while suggesting learning objects that will be interesting for them according to their needs. The objective of this contribution is to design a new mediator recommendation model for e-learning platforms to suggest learning objects to the learner based on collaborative filtering. To this end, the proposed system relies on the implicit behaviors estimation function as an underlying technique to convert tacit traces into explicit preferences allowing to compute the similarity between learners.

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A Comparative Study Between K-Nearest Neighbors and K-Means Clustering Techniques of Collaborative Filtering in e-Learning Environment

Cited by 8 publications

References 15 publications

Enhancing Online Learning

Enhancing Online Learning

Personalized E-Learning Recommender System Based on Autoencoders

Trace Learners Clustering to Improve Learning Object Recommendation in Online Education Platforms

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