Scalable and practical One-Pass clustering algorithm for recommender system

Khalid, Asra; Ghazanfar, Mustansar Ali; Azam, Muhammad Awais; Aldhafiri, Yasmeen Fahad; Zahra, Sobia

doi:10.3233/ida-150316

Cited by 8 publications

(2 citation statements)

References 26 publications

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“…NoR-MOOCs processes each data point only once hence it requires less memory and time. It is a local learning algorithm which is similar to Platt [ 15 ] and a modified model of Bassam [ 16 ] and [ 31 ]. It is efficient, scalable, incremental and generalizes well while handling large data sets.…”

Section: Novel Online Recommendation Algorithm For Massive Open Onlmentioning

confidence: 99%

Novel online Recommendation algorithm for Massive Open Online Courses (NoR-MOOCs)

et al. 2021

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Massive Open Online Courses (MOOCs) have gained in popularity over the last few years. The space of online learning resources has been increasing exponentially and has created a problem of information overload. To overcome this problem, recommender systems that can recommend learning resources to users according to their interests have been proposed. MOOCs contain a huge amount of data with the quantity of data increasing as new learners register. Traditional recommendation techniques suffer from scalability, sparsity and cold start problems resulting in poor quality recommendations. Furthermore, they cannot accommodate the incremental update of the model with the arrival of new data making them unsuitable for MOOCs dynamic environment. From this line of research, we propose a novel online recommender system, namely NoR-MOOCs, that is accurate, scales well with the data and moreover overcomes previously recorded problems with recommender systems. Through extensive experiments conducted over the COCO data-set, we have shown empirically that NoR-MOOCs significantly outperforms traditional KMeans and Collaborative Filtering algorithms in terms of predictive and classification accuracy metrics.

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Section: Novel Online Recommendation Algorithm For Massive Open Onlmentioning

confidence: 99%

Novel online Recommendation algorithm for Massive Open Online Courses (NoR-MOOCs)

et al. 2021

Self Cite

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“…The proposed algorithm processes each data point only once, so it requires less memory and time. It is a local learning algorithm (253), which is similar to Platt ( 254) and a modified model of Bassam (255) and (256). It is efficient, scalable, incremental, and generalizes well for large data sets.…”

Section: Overviewmentioning

confidence: 99%

Scalable and Practical Recommender System in Massive Open Online Courses (MOOCs)

Khalid

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Massive Open Online Courses (MOOCs) have witnessed a surge in popularity among learners and providers, prompting researchers to explore the enhancement of MOOC utilisation through recommendation systems. However, while existing efforts primarily focus on designing recommender systems capable of providing recommendations across diverse areas of MOOCs, less attention is given to essential characteristics of a robust recommender system, such as practicality and scalability. This literature gap leads to a lack of practical and scalable recommender systems capable of effectively handling increasing volumes of data. Furthermore, current systems leveraging neural networks and deep learning for user behavioural data-based recommendations face challenges in accommodating new data points without retraining. Consequently, scalable and practical recommendation algorithms for MOOCs remain limited in the current literature.To address these shortcomings, this research aims to contribute to the development of scalable and practical recommender systems for MOOCs by utilising demographic and behavioural data of learners, alongside the unique characteristics of MOOCs, for personalised recommendations. In line with these objectives, the study introduced the Novel online Recommender system for MOOCs (NoR-MOOCs) specifically designed for course recommendation. Preliminary studies on the COCO dataset demonstrate the algorithm's promising performance. NoR-MOOCs constructs user profiles based on course ratings provided by users, and its performance evaluation employs predictive and classification accuracy metrics (RMSE, ROC, precision, recall and coverage). A comparison with traditional K-Means and collaborative Filtering algorithms validates the effectiveness of NoR-MOOCs.Additionally, considering the abundant user behavioural and demographic data available in MOOCs, a Novel online Multi-attribute-based Recommendation algorithm for MOOCs (NoM-MOOCs) is designed. This algorithm creates user profiles based on their behavioural and demographic characteristics. Extensive experiments on the Edx and CAN datasets demonstrate the algorithm's significant out-performance compared to the Kernal Mapping recommender system (KMR) in terms of predictive and classification accuracy metrics. Moreover, the analysis of behavioural data from MOOCs logs contributes to forming valuable learning pathways for learners. Existing literature has extensively explored the design and implementation of learning pathway recommendation systems, with such recommender systems being intricately designed using neural networks and deep learning. To this end, a fast online learning path recommender system that incorporates demographic and behavioural data of learners to recommend learning paths is designed. This system identifies similar learners by leveraging demographics and behavioural data and recommends pats paths taken by successful students to struggling students with similar behavioural and demographic profiles. To evaluate the results of this proposed algorithm and the potential impact of the learning path recommender system on learners, an interview with the MOOC professor is conducted.Overall, this research strives to contribute to the advancement of scalable and practical recommender systems for MOOCs, enriching the learning experiences of users and promoting the effective utilisation of MOOCs platforms.

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Evolution of recommender system over the time

Sinha

Dhanalakshmi²

2019

Soft Comput

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Scalable and practical One-Pass clustering algorithm for recommender system

Cited by 8 publications

References 26 publications

Novel online Recommendation algorithm for Massive Open Online Courses (NoR-MOOCs)

Novel online Recommendation algorithm for Massive Open Online Courses (NoR-MOOCs)

Scalable and Practical Recommender System in Massive Open Online Courses (MOOCs)

Evolution of recommender system over the time

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