A Reputation-Enhanced Recommender System

Abdel-Hafez, Ahmad; Tang, Xiaoyu; Tian, Nan; Xu, Yue

doi:10.1007/978-3-319-14717-8_15

Cited by 11 publications

(12 citation statements)

References 21 publications

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“…Please note that Abdel-Hafez et. al [1] describe two distinct hybridization approaches in their paper.…”

Section: Overview Of Existing Workmentioning

confidence: 99%

“…Abdel-Hafez et al [1] describe a weighted hybridization method in which the recommender and the reputation system use the same data base. The first step is to perform the Borda count method separately for the ranked output lists of the recommender system and the reputation system.…”

Section: Weightedmentioning

confidence: 99%

“…In contrast, Abdel-Hafez et al [1] consider a cascade hybridization of a recommender and a reputation system relying on the same data base. They enhance a recommender system's output by re-sorting the top-M recommendations based on their reputation values.…”

Section: Rec-rep-cascadementioning

confidence: 99%

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State of the art of reputation-enhanced recommender systems

Richthammer

Weber

Pernul

2018

WEB

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Recommender systems are pivotal components of modern Internet platforms and constitute a well-established research field. By now, research has resulted in highly sophisticated recommender algorithms whose further optimization often yields only marginal improvements. This paper goes beyond the commonly dominating focus on optimizing algorithms and instead follows the idea of enhancing recommender systems with reputation data. Since the concept of reputation-enhanced recommender systems has attracted considerable attention in recent years, the main aim of the paper is to provide a comprehensive survey of the approaches proposed so far. To this end, existing work are identified by means of a systematic literature review and classified according to seven carefully considered dimensions. In addition, the resulting structured analysis of the state of the art serves as a basis for the deduction and discussion of several future research directions.

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“…Please note that Abdel-Hafez et. al [1] describe two distinct hybridization approaches in their paper.…”

Section: Overview Of Existing Workmentioning

confidence: 99%

Section: Weightedmentioning

confidence: 99%

See 1 more Smart Citation

State of the art of reputation-enhanced recommender systems

Richthammer

Weber

Pernul

2018

WEB

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show abstract

“…Then, the sum BC is ( ) = ( ) + ( ). The Top-N recommendations for the user u are selected using equation (5).…”

Section: 21mentioning

confidence: 99%

“…We use the weighted Borda Count method [5] to merge two sorted lists generated by recommender and reputation systems. However, the accuracy of this method could be reduced by some noisy items.…”

Section: Introductionmentioning

confidence: 99%

Item Reputation-Aware Recommender Systems

Abdel-Hafez

Tian

2014

Proceedings of the 16th International Conference on Information Integration and Web-Based Applications &Amp; Services

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Recommender systems provide personalized advice for online customers based on their own preferences, while reputation systems generate a community advice on the quality of items on the Web. Both systems employ users' ratings to generate their output. In this paper, we aim to combine reputation models with recommender systems to enhance the accuracy of recommendations. Our proposed methods make two contributions. First of all, we propose two methods for merging two ranked item lists which are generated based on recommendation scores and reputation scores, respectively. In addition, a novel personalized reputation method is designed in order to generate item reputations based upon users' interests. The proposed merging methods can be applicable to any recommendation methods and reputation methods, i.e., they are independent from generating recommendation scores and reputation scores. The experiments we conducted showed that the proposed methods could enhance the accuracy of existing recommender systems.

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Exploiting the Beta Distribution-Based Reputation Model in Recommender System

Abdel-Hafez

2015

AI 2015: Advances in Artificial Intelligence

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A Reputation-Enhanced Recommender System

Cited by 11 publications

References 21 publications

State of the art of reputation-enhanced recommender systems

State of the art of reputation-enhanced recommender systems

Item Reputation-Aware Recommender Systems

Exploiting the Beta Distribution-Based Reputation Model in Recommender System

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