Exploiting the beta distribution-based reputation model in recommender system

Abdel-Hafez, Ahmad & Xu, Yue (2015) Exploiting the beta distribution-based reputation model in recommender system. In Lecture Notes in Computer Science, Springer Berlin Heidelberg, Canberra, A.C.T, pp. 1-13.

View at publisher


Reputation systems are employed to measure the quality of items on the Web. Incorporating accurate reputation scores in recommender systems is useful to provide more accurate recommendations as recommenders are agnostic to reputation. The ratings aggregation process is a vital component of a reputation system. Reputation models available do not consider statistical data in the rating aggregation process. This limitation can reduce the accuracy of generated reputation scores. In this paper, we propose a new reputation model that considers previously ignored statistical data. We compare our proposed model against state-of the-art models using top-N recommender system experiment.

Impact and interest:

0 citations in Scopus
1 citations in Web of Science®
Search Google Scholar™

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

Full-text downloads:

6 since deposited on 08 Feb 2016
5 in the past twelve months

Full-text downloads displays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.

ID Code: 92704
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
Keywords: Reputation System, Ratings Aggregation, Beta Distribution, Recommender System
DOI: 10.1007/978-3-319-26350-2_1
ISBN: 9783319263502
ISSN: 0302-9743
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2015 Springer International Publishing Switzerland
Deposited On: 08 Feb 2016 22:06
Last Modified: 13 Dec 2016 15:34

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page