2020
DOI: 10.3390/app10072204
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Recommender System Based on Temporal Models: A Systematic Review

Abstract: Over the years, the recommender systems (RS) have witnessed an increasing growth for its enormous benefits in supporting users’ needs through mapping the available products to users based on their observed interests towards items. In this setting, however, more users, items and rating data are being constantly added to the system, causing several shifts in the underlying relationship between users and items to be recommended, a problem known as concept drift or sometimes called temporal dynamics in RS. Althoug… Show more

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Cited by 35 publications
(23 citation statements)
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References 132 publications
(234 reference statements)
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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 & Zellou, 2020;Chu et al, 2020), drift issue (Rabiu et al, 2020;Al-Hadi et al, 2017a) and time decay issue (Koren, 2009;Ye & 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: 98%
“…Summarily, the existing temporal-based approaches have addressed several limitations of recommender systems such as sparsity (Zhang et al, 2020a;Idrissi & Zellou, 2020;Chu et al, 2020), drift issue (Rabiu et al, 2020;Al-Hadi et al, 2017a) and time decay issue (Koren, 2009;Ye & 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: 98%
“…α ≥ 1). In the second term, the inversed substitution between two nodes' attributes is adopted to produce the similarity as a weight value (see Equations (7) and (8)). The attributes of nodes have been normalized to remove scale dependency.…”
Section: Graph-based Domain Transfermentioning
confidence: 99%
“…However, recent academic studies and industrial providers have both emphasized that one of the main measurement targets is improved user retention [5,6]. In spite of the gains in theoretical accuracy, it was unclear that the winning strategy in accuracy would result always in increasing business value [7].…”
Section: Introductionmentioning
confidence: 99%
“…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%