A rating aggregation method for generating product reputations

Abdel-Hafez, Ahmad, Xu, Yue, & Josang, Audun (2014) A rating aggregation method for generating product reputations. In Proceedings of the 25th ACM Conference on Hypertext and Social Media, ACM Digital Library, Santiago, Chile, pp. 291-293.

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Many websites offer the opportunity for customers to rate items and then use customers' ratings to generate items reputation, which can be used later by other users for decision making purposes. The aggregated value of the ratings per item represents the reputation of this item. The accuracy of the reputation scores is important as it is used to rank items.

Most of the aggregation methods didn't consider the frequency of distinct ratings and they didn't test how accurate their reputation scores over different datasets with different sparsity. In this work we propose a new aggregation method which can be described as a weighted average, where weights are generated using the normal distribution. The evaluation result shows that the proposed method outperforms state-of-the-art methods over different sparsity datasets.

Impact and interest:

5 citations in Scopus
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ID Code: 76398
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
Keywords: Reputation Model, Ratings Aggregation, Uncertainty
DOI: 10.1145/2631775.2631779
ISBN: 9781450329545
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > INFORMATION SYSTEMS (080600) > Decision Support and Group Support Systems (080605)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > LIBRARY AND INFORMATION STUDIES (080700) > Information Retrieval and Web Search (080704)
Divisions: Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2014 The Authors
Copyright Statement: Permission to make digital or hard copies of part or all of this work for personal or
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Deposited On: 29 Sep 2014 22:37
Last Modified: 27 Jun 2017 16:23

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