Exploiting the Beta Distribution-Based Reputation Model in Recommender System

Abdel-Hafez, Ahmad; Xu, Yan

doi:10.1007/978-3-319-26350-2_1

Cited by 7 publications

(12 citation statements)

References 13 publications

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“…In this study we used eight state-of-art models are: Mean, Median, BetaDR [1], Bayesian [6], Dirichlet [5], IMDb, Fuzzy rating [2] and LQ [7]. For comparison purpose we used 10-Fold cross validation.…”

Section: Methodsmentioning

confidence: 99%

“…, -is a compensation factor determined by expert. Abdel-Hafez et al [1], [16] used Beta distribution function to compute ratings weights. Their model is called BetaDR.…”

Section: Overviewmentioning

confidence: 99%

“…They help users to facilitate their decisions while they perform internet transactions [1], [4]. The online rating system is responsible for collecting, processing and aggregating ratings given for a specific product.…”

Section: Introductionmentioning

confidence: 99%

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Comparative Analysis of Online Rating Systems

Azzeh¹

2017

ijacsa

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Abstract-Online rating systems serve as decision support tool for choosing the right transactions on the internet. Consumers usually rely on others' experiences when do transaction on the internet, therefore their feedbacks are helpful in succeeding such transactions. One important form of such feedbacks is the product ratings. Most online rating systems have been proposed either by researchers or industry. But there is much debate about their accuracies and stability. This paper looks at the accuracy and stability of set of common online rating systems over dense and sparse datasets. To accomplish that we used three evaluation measures namely, Mean Absolute Errors (MAE), Mean Balanced Relative Error (MBRE) and Mean Inverse Balanced Relative Error (MIBRE), in addition to Borda count to assess the stability of ranking among various rating systems. The results showed that both median and Dirichlet are the most accurate models for both sparse and dense datasets, whereas the BetaDR model is the most stable model across different evaluation measures. Therefore we recommend using Dirichlet or BetaDR for the products with few number of ratings and using the median model with products of large number of ratings.

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Section: Methodsmentioning

confidence: 99%

“…, -is a compensation factor determined by expert. Abdel-Hafez et al [1], [16] used Beta distribution function to compute ratings weights. Their model is called BetaDR.…”

Section: Overviewmentioning

confidence: 99%

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Comparative Analysis of Online Rating Systems

Azzeh¹

2017

ijacsa

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“…E-commerce and mobile commerce systems are increasingly growing in the last two decades which resulted in emergence of new technologies and services [1][6] [10]. Therefore, the internet turns into the most common workspace for performing our transactions such as selling and purchasing goods.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore an accurate and reliable reputation system has moved from novelty and convince to necessity. Almost all B2C and C2C websites ask users to provide ratings and reviews after any successful transaction [1]. For example, eBay users can rate each other, while other review websites, user can rate other reviews as helpful or not.…”

Section: Introductionmentioning

confidence: 99%

Online Reputation Model Using Moving Window

Azzeh¹

2017

ijacsa

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Abstract-Users are increasingly dependent on decision tools to facilitate their transactions on the internet. Reputation models offer a solution to the users in supporting their purchase decisions. The reputation model takes product ratings as input and produces product quality as score. Most existing reputation models use naïve average method or weighted average method to aggregate ratings. Naïve average method is unstable when there exist a clear trend in the ratings sequence. Also, the weighted methods are influenced by unfair and malicious ratings. This paper introduces a new simple reputation model that aggregates ratings based on the concept of moving window. This approach enables us to study variability of ratings over time which allows us to investigate the trend of ratings and account for sudden changes in ratings trend. The window size can be defined by either number of ratings or duration. The proposed model has been validated against stat-ofart reputation models using Mean Absolute Error and Kendall tau correlation.

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V2V Communication and Authentication: The Internet of Things Vehicles(Iotv)

Peter

Rani

2021

Wireless Pers Commun

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Exploiting the Beta Distribution-Based Reputation Model in Recommender System

Cited by 7 publications

References 13 publications

Comparative Analysis of Online Rating Systems

Comparative Analysis of Online Rating Systems

Online Reputation Model Using Moving Window

V2V Communication and Authentication: The Internet of Things Vehicles(Iotv)

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