Collusion detection in online rating systems

Mohammad Allahbakhsh*, Aleksandar Ignjatovic, Boualem Benatallah, Seyed Mehdi Reza Beheshti, Elisa Bertino, Norman Foo

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

22 Citations (Scopus)


Online rating systems are subject to unfair evaluations. Users may try to individually or collaboratively promote or demote a product. Collaborative unfair rating, i.e., collusion, is more damaging than individual unfair rating. Detecting massive collusive attacks as well as honest looking intelligent attacks is still a real challenge for collusion detection systems. In this paper, we study impact of collusion in online rating systems and asses their susceptibility to collusion attacks. The proposed model uses frequent itemset mining technique to detect candidate collusion groups and sub-groups. Then, several indicators are used for identifying collusion groups and to estimate how damaging such colluding groups might be. The model has been implemented and we present results of experimental evaluation of our methodology.

Original languageEnglish
Title of host publicationWeb Technologies and Applications
Subtitle of host publication15th Asia-Pacific Web Conference, APWeb 2013, Proceedings
EditorsYoshiharu Ishikawa, Jianzhong Li, Wei Wang, Rui Zhang
PublisherSpringer, Springer Nature
Number of pages12
ISBN (Electronic)9783642374012
ISBN (Print)9783642374005
Publication statusPublished - 10 Apr 2013
Externally publishedYes
Event15th Asia-Pacific Web Conference on Web Technologies and Applications, APWeb 2013 - Sydney, NSW, Australia
Duration: 4 Apr 20136 Apr 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7808 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference15th Asia-Pacific Web Conference on Web Technologies and Applications, APWeb 2013
CitySydney, NSW

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