Item reputation-aware recommender systems

Abdel-Hafez, Ahmad, Xu, Yue, & Tian, Nan (2014) Item reputation-aware recommender systems. In IIWAS '14 : Proceedings of International Conference on Information Integration and Web-based Applications & Services, ACM Digital Library, Hanoi, Vietnam. (In Press)

View at publisher


Recommender systems provide personalized advice for customers online based on their own preferences, while reputation systems generate a community advice on the quality of items on the Web. Both systems use users’ ratings to generate their output. In this paper, we propose to combine reputation models with recommender systems to enhance the accuracy of recommendations. The main contributions include two methods for merging two ranked item lists which are generated based on recommendation scores and reputation scores, respectively, and a personalized reputation method to generate item reputations based on 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.

Impact and interest:

2 citations in Scopus
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:

48 since deposited on 28 Jan 2015
10 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: 78819
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
Keywords: Recommender System, Reputation System, User profile, Personalized Reputation, Merging Ranked Lists
DOI: 10.1145/2684200.2684301
ISBN: 9781450330015
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 > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2014 ACM
Deposited On: 28 Jan 2015 02:49
Last Modified: 19 Mar 2015 23:32

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page