Publication Type

Journal Article

Version

acceptedVersion

Publication Date

11-2008

Abstract

Evaluation is prevalent in real life. With the advent of Web 2.0, online evaluation has become an important feature in many applications that involve information (e.g., video, photo, and audio) sharing and social networking (e.g., blogging). In these evaluation settings, a set of reviewers assign scores to a set of objects. As part of the evaluation analysis, we want to obtain fair reviews for all the given objects. However, the reality is that reviewers may deviate in their scores assigned to the same object, due to the potential bias of reviewers or controversy of objects. The statistical approach of averaging deviations to determine bias and controversy assumes that all reviewers and objects should be given equal weight. In this paper, we look beyond this assumption and propose an approach based on the following observations: 1) evaluation is subjective, as reviewers and objects have varying bias and controversy, respectively, and 2) bias and controversy are mutually dependent. These observations underlie our proposed reinforcement-based model to determine bias and controversy simultaneously. Our approach also quantifies evidence, which reveals the degree of confidence with which bias and controversy have been derived. This model is shown to be effective by experiments on real-life and synthetic data sets.

Keywords

Computer Applications, Information Technology and Systems Applications, Social and Behavioral Sciences

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Publication

IEEE Transactions on Knowledge and Data Engineering

Volume

20

Issue

11

First Page

1490

Last Page

1504

ISSN

1041-4347

Identifier

10.1109/tkde.2008.77

Publisher

IEEE

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/tkde.2008.77

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