Temporal integration based factorization to improve prediction accuracy of collaborative filtering by Al-Qasem, Al-Hadi Ismail Ahmed

“…The rating matrix typically contains a high percentage of unknown rating scores which is called the data sparsity problem. The data sparsity problem has been solved by several approaches such as Bayesian probabilistic, machine learning, genetic algorithm, particle swarm optimization and matrix factorization. …”
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Temporal-based approach to solve item decay problem in recommendation system by Al-Qasem, Al-Hadi Ismail Ahmed, Mohd Sharef, Nurfadhlina, Sulaiman, Md. Nasir, Mustapha, Norwati

“…The rating matrix of a recommendation system contains a high percentage of data sparsity which lowers the prediction accuracy of the collaborative filtering technique (CF). …”
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A temporal-focused trustworthiness to enhance trust-based recommender systems by Moghaddam, Morteza Ghorbani, Mustapha, Aida, Mustapha, Norwati, Mohd Sharef, Nurfadhlina

“…Implementation of the proposed approach is hoped to reduce cold-start and sparsity as well as improve accuracy of the recommendation results.…”
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A novel temporal trust-based recommender system by Moghaddam, Morteza Ghorbani, Elahian, Anousheh

“…While Collaborative Filtering (CF) recommender systems, which focus on previous indicate preferences, are known for their traditional problems such as cold-start, sparsity and modest accuracy, trust-based CF has been previously proposed to solve such issues by focusing on trust values among the users. …”
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AgeTrust: a new temporal trust-based collaborative filtering approach by Moghaddam, Morteza Ghorbani, Mustapha, Norwati, Mustapha, Aida, Mohd Sharef, Nurfadhlina, Elahian, Anousheh

“…This approach focuses on previous indicate preferences which is known for its traditional problems such as cold-start, sparsity and hacking. For solving the problem of hacking and improving the accuracy, trust-based CF methods have been proposed previously. …”

An efficient traffic state estimation model based on fuzzy C-mean clustering and MDL using FCD by Ahanin, Fatemeh, Mustapha, Norwati, Sulaiman, Nasir, Zolkepli, Maslina

“…However, FCD suffers from data sparseness and many researches have been done to improve traffic estimation accuracy with respect to data sparsity. In this paper, a new model based on Fuzzy C-Mean (FCM) clustering and Minimum Description Length (MDL) is proposed to estimate the missing traffic state using FCD. …”
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Fuzzy AHP and TOPSIS in cross domain collaboration Recommendation with fuzzy visualization representation by Zolkepli, Maslina, Mohd Aris, Teh Noranis

“…Existing cross-domain recommendation tackles the problem of sparsity, scalability, cold-start and serendipity issues found in single-domain, therefore the combination of fuzzy AHP and TOPSIS with visualization method may be able to give decision makers a quick start to initiate cross-domain collaborations. …”

Web service applications and consumer environments based on ICT-driven optimization by Fan, Chaozhi, Law, Siong Hook, Ibrahim, Saifuzzaman, Ahmad, Mohd Naseem

“…In the field of web service application and consumer environment optimization, it has been shown that the introduction of network embedding methods can effectively alleviate the problems such as data sparsity in the recommendation process. However, existing network embedding methods mostly target a specific structure of network and do not collaborate with multiple relational networks from the root. …”

ROME: a graph contrastive multi-view framework from hyperbolic angular space for MOOCs recommendation by Luo, Hao, Husin, Nor Azura, Mohd Aris, Teh Noranis

“…However, all existing graph models degrade performances either by ignoring the data sparsity issue caused by a large number of concepts, which may lead to biased recommendations, or by constructing improper contrasting pairs, which may result in graph noise. …”

Embedded learning for leveraging multi-aspect in rating prediction of personalized recommendation by Khairudin, Nurkhairizan, Mohd Sharef, Nurfadhlina, Mohd Noah, Shahrul Azman, Mustapha, Norwati

“…However, the most challenging difficulty of this approach is the lack of sufficient ratings or the so-called data sparsity. Moreover, sometimes these ratings alone are not sufficient to precisely understand users' specific behaviours. …”
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Parallel Diagonally Implicit Runge-Kutta Methods For Solving Ordinary Differential Equations by Din, Ummul Khair Salma

“…A few new methods are proposed by having sparsity patterns which enable the parallelization of methods. …”
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Ensemble divide and conquer approach to solve the rating scores’ deviation in recommendation system by Al-Hadi, Ismail Ahmed Al-Qasem, Mohd Sharef, Nurfadhlina, Sulaiman, Md Nasir, Mustapha, Norwati

“…Therefore, this paper proposes Ensemble Divide and Conquer (EDC) approach for solving 2 main problems which are the data sparsity and the rating scores’ deviation (misplace). …”
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Developmental idealism and cultural extinction in the selected works of Chuma Nwokolo by Nkeokelonye, Adaobi

“…The struggle of minority people to sustain their culture in the face of the pressures of development is also captured in literary works. Notwithstanding, a sparsity of studies on literature that have substantively covered these phenomena and the remedial measures for addressing it, is observed. …”
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Denoising of digital images using second generation wavelet transforms-hidden markov model by Khmag, Asem Ib Mohamed

“…In this regard, the second-generation wavelets (SGWs) that employ the properties of sparsity and multiresolution from discrete wavelet transformation (DWT) is used in noise reduction. …”
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Feature extraction based on word embeddings and opinion lexicals for sentiment analysis by Alshari, Eissa Mohammed Mohsen

“…Secondly, many feature extraction techniques have been proposed to alleviate the data density and sparsity issue by mean of feature clustering. Such methods often result in the reduction of vector dimension and assign a more effective weighting scheme to improve the efficiency and effectiveness of sentiment analysis. …”
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