Ensemble Divide and Conquer Approach to Solve the Rating Scores&amp;rsquo; Deviation in Recommendation System

Al-Hadi, Ismail Ahmed Al-Qasem; Sharef, Nurfadhlina Mohd; Sulaiman, Nasir; Mustapha, Norwati

doi:10.3844/jcssp.2016.265.275

Cited by 8 publications

(15 citation statements)

References 22 publications

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“…To anticipate the sparse scores rating, µ, B u , B i , p u , and q T i are integrated in numerous mathematical equations such as those in temporal approaches (Koren, 2009;Ye & Eskenazi, 2014) and factorization methods (Al-Hadi et al, 2016;Han et al, 2018;Yuan, Zahir & Yang, 2019). For instance, the baseline factor and the distance between rating scores and baseline values of neighbors who supply their rating scores for each product are combined by the neighbors based baseline method (Bell & Koren, 2007) as presented in Eq.…”

Section: Related Work Collaborative Filteringmentioning

confidence: 99%

“…The first part contains the prediction accuracy of the CF technique by RMSE for the rating matrix of the active user without predicting the sparse rating scores. The second part contains the prediction accuracy of the CF by two factorization approaches which are Neighbour-based Baseline (Bell & Koren, 2007) and the Ensemble Divide and Conquer (Al-Hadi et al, 2016). These approaches are used to solve the sparsity issue and as well learn the accurate factorization features.…”

Section: Comparison Of the Performances Of Cf Mf And Temporal-basedmentioning

confidence: 99%

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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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Section: Related Work Collaborative Filteringmentioning

confidence: 99%

Section: Comparison Of the Performances Of Cf Mf And Temporal-basedmentioning

confidence: 99%

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

Al-Hadi

Sharef

Sulaiman

et al. 2020

PeerJ Computer Science

Self Cite

View full text Add to dashboard Cite

show abstract

“…A method based on ensemble divide and conquer (Al-Hadi et al, 2016) was adopted to solve the misplacement problem besides addressing the customers' preferences drift and popularity decay.…”

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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“…Therefore, this work introduces the LTO approach for learning the features related to all these issues. Neighbors-based Baseline (Bell & Koren, 2007) Temporal Dynamics (Koren, 2009) Ensemble Divide and Conquer (Al-Hadi et al, 2016) Short-Term based Latent (Yang et al, 2012) Temporal Integration (Ye & Eskenazi, 2014) Long Temporal-based Factorization (Al-Hadi et al, 2018b)…”

Section: The Short-term Preferencesmentioning

confidence: 99%

Supplemental Information 1: LTO approach

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Ensemble Divide and Conquer Approach to Solve the Rating Scores’ Deviation in Recommendation System

Abstract: access article is distributed under a Creative Commons Attribution (CC-BY) 3.0 license.

Cited by 8 publications

References 22 publications

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

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)"

Supplemental Information 1: LTO approach

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