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Estimating global opinions by keeping users from fraud in online review systems

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Abstract

In this work, we focus on online review systems, in which users provide opinions about a set of entities (movies, restaurants, etc.) based on their experiences and in turn can check what others prefer. These systems have been proved to be sensitive to fraud and have shown some shortcomings as a result of capturing opinions through numerical ratings. Thus, supported by recent work on the field, we tackle the problem of fraud in such systems by designing a mechanism based on pairwise comparisons, coupled with an incentive policy attempting to foster the collection of majority opinions over individual experiences. As a result, we propose a new mechanism called iPWRM (incentive-based PWRM), where users are persuaded to reply honestly to pairwise queries based on opinion polls. The idea is: (1) to give a positive reward when all users agree in their reviews; (2) to give a positive reward when a user agrees the majority’s choice; and finally, (3) to give a low incentive—possibly null—when user’s review does not match the majority. Therefore, it is able (1) to overcome the bias introduced into reputation rankings by fraud reviews in ORSs, as well as (2) to mitigate potential biased problems derived from the use of numerical ratings. We exhaustively test the performance of the mechanism by using two different well-known existing datasets Flixster and HetRec2011—real world datasets on movie reviews, aiming to test the performance of the mechanism as well as the effectiveness and efficiency of iPWRM when fraud comes into play.

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Notes

  1. For every match, a different set of users may be chosen. Moreover, note the size of this subset is very small compared to the size of the potential users that might be queried.

  2. It is important to remark that reward does not have to be necessarily money based, but it might be points, virtual money, or any other resource considered as valuable for users.

  3. http://www.cs.ubc.ca/~jamalim/datasets/.

  4. http://www.flixster.com.

  5. http://ir.ii.uam.es/hetrec2011.

  6. http://www.grouplens.org.

  7. http://www.movielens.org/.

  8. It is assumed users always reply a match query.

  9. Note that a detailed research on the performance of different configurations for PWRM set-up can be found in the works presented by Centeno et al. [4] and Hermoso et al. [14].

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Acknowledgements

We thank anonymous reviewers for their very useful comments and suggestions. The work was supported by eMadrid project S2013-ICE-2715, Spanish Ministry of Economy and Competitiveness (TIN2012-36586-C03-02-iHAS) and by the Autonomous Region of Madrid (P2013/ICE-3019-MOSI-AGIL-CM, co-funded by EU-FSE and FEDER”).

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Authors and Affiliations

  1. Department of Computer Languages and Systems, UNED, Madrid, Spain

    Roberto Centeno

  2. Department of Informatics and Systems Engineering, University of Zaragoza, Zaragoza, Spain

    Ramón Hermoso

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  1. Roberto Centeno
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  2. Ramón Hermoso
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Correspondence to Roberto Centeno.

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Centeno, R., Hermoso, R. Estimating global opinions by keeping users from fraud in online review systems. Knowl Inf Syst 55, 467–491 (2018). https://doi.org/10.1007/s10115-017-1089-2

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