Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/48491
Title: Detecting unfair rating attacks in online rating systems
Authors: Lee, Chin Hwee.
Keywords: DRNTU::Engineering::Computer science and engineering::Information systems
Issue Date: 2012
Abstract: If you ever buy thing online from an unknown seller, the seller’s rating information that is given to you, how certain are you to trust the seller? The seller’s rating that you had saw on the internet may or may not be the actual reflection of the seller trustworthiness. In order to help buyers with their purchasing decision, this has given research community to work on the effectiveness in detecting unfair rating on the online system. In this report, it will discuss two existing detecting unfair rating models which are BRS and TRAVOS. To further help the researcher in these areas to evaluate the effectiveness of the detecting models, it will also be discussed on the development of a marketplace simulation where researcher can run a virtual marketplace simulation where buyers and sellers can do transactions.
URI: http://hdl.handle.net/10356/48491
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

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SCE11-0370.pdf
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In this project, you will be learning some classic algorithms for detecting unfair rating attacks in online rating systems. After this, it is expected for you to design and develop an algorithm of your own that will have better performance in detecting unfair ratings.1.18 MBAdobe PDFView/Open
SCE11-0370a.pdf
  Restricted Access
Software Requirement Specification2.58 MBAdobe PDFView/Open

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