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The ongoing rapid development of the e-commercial and interest-base websitesmake it more pressing to evaluate objects' accurate quality beforerecommendation by employing an effective reputation system. The objects'quality are often calculated based on their historical information, such asselected records or rating scores, to help visitors to make decisions beforewatching, reading or buying. Usually high quality products obtain a higheraverage ratings than low quality products regardless of rating biases orerrors. However many empirical cases demonstrate that consumers may be misledby rating scores added by unreliable users or deliberate tampering. In thiscase, users' reputation, i.e., the ability to rating trustily and precisely,make a big difference during the evaluating process. Thus, one of the mainchallenges in designing reputation systems is eliminating the effects of users'rating bias on the evaluation results. To give an objective evaluation of eachuser's reputation and uncover an object's intrinsic quality, we propose aniterative balance (IB) method to correct users' rating biases. Experiments ontwo online video-provided Web sites, namely MovieLens and Netflix datasets,show that the IB method is a highly self-consistent and robust algorithm and itcan accurately quantify movies' actual quality and users' stability of rating.Compared with existing methods, the IB method has higher ability to find the"dark horses", i.e., not so popular yet good movies, in the Academy Awards.

Eliminating the effect of rating bias on reputation systems