A novel recommendation system via L0-regularized convex optimization

Jia-jun, Lin; Li, Yibin; Lian, Jian

doi:10.1007/s00521-019-04213-w

Cited by 15 publications

(16 citation statements)

References 19 publications

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“…Lin et al [61] Complementary recommendation structure for freshmen under restrictions or requirements, based on objective-oriented standards. Niknam and Thulasiraman [63] Intelligent learning path recommendation based on meaningful learning theory.…”

Section: Recommendation Systemsmentioning

confidence: 99%

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A Literature Review on Intelligent Services Applied to Distance Learning

Silva¹,

Dias²,

Rigo³

et al. 2021

Preprint

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Distance learning has assumed a relevant role in the Educational scenario. The use of Virtual Learning Environments contributes to obtain a substantial amount of educational data. In this sense, the analyzed data generate knowledge used by institutions to assist managers and professors in strategic planning and teaching. The discovery of students’ behaviors enables a wide variety of intelligent services for assisting in the learning process. This article presents a literature review in order to identify the intelligent services applied in distance learning. The research covers the period from January 2010 to May 2021. The initial search found 1,316 articles, among which 51 were selected for further studies. Considering the selected articles, 33% (17/51) focus on learning systems, 35% (18/51) propose recommendation systems, 26% (13/51) approach predictive systems or models, and 6% (3/51) use assessment tools. This review allowed to observe that the principal services offered are recommendation systems and learning systems. In these services, the analysis of student profiles stands out to identify patterns of behavior, detect low performance and identify probabilities of dropouts from courses.

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Section: Recommendation Systemsmentioning

confidence: 99%

“…Lin et al [61] proposed a complementary recommendation framework for freshmen under constraints or requirements, based on goal-oriented standards. Students can get the results of recommendations according to different types of learning objectives.…”

Section: Recommendation Systemsmentioning

confidence: 99%

A Literature Review on Intelligent Services Applied to Distance Learning

Silva¹,

Dias²,

Rigo³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Recommendation systems are some of the most powerful methods for suggesting products to customers based on their interests and online purchases (Jonnalagedda et al, 2016;Lin, Li & Lian, 2020;Nilashi, bin Ibrahim & Ithnin, 2014;Nilashi et al, 2015;Zhang et al, 2020b). In terms of personalization of recommendations, one of the most prevalently used methods is collaborative filtering (CF) (Nilashi, bin Ibrahim & Ithnin, 2014;Sardianos, Ballas Papadatos & Varlamis, 2019;Nilashi et al, 2015;Wu et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Latent based temporal optimization approach for improving the performance of collaborative filtering

Al-Hadi

Sharef

Sulaiman

et al. 2020

PeerJ Computer Science

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Recommendation systems suggest peculiar products to customers based on their past ratings, preferences, and interests. These systems typically utilize collaborative filtering (CF) to analyze customers’ ratings for products within the rating matrix. CF suffers from the sparsity problem because a large number of rating grades are not accurately determined. Various prediction approaches have been used to solve this problem by learning its latent and temporal factors. A few other challenges such as latent feedback learning, customers’ drifting interests, overfitting, and the popularity decay of products over time have also been addressed. Existing works have typically deployed either short or long temporal representation for addressing the recommendation system issues. Although each effort improves on the accuracy of its respective benchmark, an integrative solution that could address all the problems without trading off its accuracy is needed. Thus, this paper presents a Latent-based Temporal Optimization (LTO) approach to improve the prediction accuracy of CF by learning the past attitudes of users and their interests over time. Experimental results show that the LTO approach efficiently improves the prediction accuracy of CF compared to the benchmark schemes.

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“…Recommendation systems are some of the most powerful methods for suggesting products to customers based on their interests and online purchases (Jonnalagedda et al, 2016;Lin et al, 2020;Nilashi et al, 2014Nilashi et al, , 2015Zhang et al, 2020b). In terms of personalization of recommendations, one of the most prevalently used methods is collaborative filtering (CF) (Nilashi et al, 2014;Sardianos et al, 2019;Nilashi et al, 2015;Wu et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Peer Review #1 of "Latent based temporal optimization approach for improving the performance of collaborative filtering (v0.1)"

Khfagy¹

2020

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A novel recommendation system via L0-regularized convex optimization

Cited by 15 publications

References 19 publications

A Literature Review on Intelligent Services Applied to Distance Learning

A Literature Review on Intelligent Services Applied to Distance Learning

Latent based temporal optimization approach for improving the performance of collaborative filtering

Peer Review #1 of "Latent based temporal optimization approach for improving the performance of collaborative filtering (v0.1)"

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