Alleviating the data sparsity problem of recommender systems by clustering nodes in bipartite networks

Zhang, Fuguo; Qi, Shumei; Liu, Qihua; Mao, Mingsong; Zeng, An

doi:10.1016/j.eswa.2020.113346

Cited by 55 publications

(25 citation statements)

References 30 publications

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“…Summarily, the existing temporal-based approaches have addressed several limitations of recommender systems such as sparsity (Zhang et al, 2020a;Idrissi and Zellou, 2020;Chu et al, 2020), drift issue (Rabiu et al, 2020;Al-Hadi et al, 2017a) and time decay issue (Koren, 2009;Ye and Eskenazi, 2014;Al-Hadi et al, 2018b). Each reviewed approach in this article has one or two research gaps, e.g., learning the personalized features, the drift preferences, and the popularity decay.…”

Section: The Short-term Preferencesmentioning

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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Section: The Short-term Preferencesmentioning

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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Section: /20mentioning

confidence: 99%

Peer Review #1 of "Latent based temporal optimization approach for improving the performance of collaborative filtering (v0.2)"

Khfagy¹

2020

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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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“…In CF, personalized prediction of products depends on the latent features of users in a rating matrix. However, the CF prediction accuracy decreases if the rating matrix is sparse (Zhang et al, 2020a;Li & Chi, 2018;Idrissi & Zellou, 2020). Several types of factorization techniques such as baseline, singular value decomposition (SVD), matrix factorization (MF), and neighbors-based baseline have been exploited to address the problem of data sparsity (Mirbakhsh & Ling, 2013;Al-Hadi et al, 2017b) by predicting the missing rating scores in the rating matrix.…”

Section: Introductionmentioning

confidence: 99%

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

Al-Hadi

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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.

show abstract

Alleviating the data sparsity problem of recommender systems by clustering nodes in bipartite networks

Cited by 55 publications

References 30 publications

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

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

Peer Review #1 of "Latent based temporal optimization approach for improving the performance of collaborative filtering (v0.2)"

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

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