Improving the Accuracy of Business-to-Business (B2B) Reputation Systems through Rater Expertise Prediction

International audienceDigital ecosystems rely on reputation systems in order to build trust and to foster collaborations among users. Reputation systems are commonplace in the C2C and B2C contexts, however, they have not yet found mainstream acceptance in B2B environments. Our first contribution in this paper is to identify the particularities of feedback collection in B2B reputation systems. An issue that we identify is that the reputation target in the B2B context is a business, which requires evaluation on a large number of criteria. We observe that due to the wide variation in user expertise, feedback forms that require users to evaluate all criteria have significant negative consequences for rating accuracy. Our second contribution is to propose an expertise prediction algorithm for B2B reputation systems, which filters the criteria describing the target business such that each user rates only on those criteria that he has expertise in. Experiments based on our real dataset show that the algorithm accurately predicts the expertise of users in given criteria. The algorithm may also increase the motivation of users to submit feedback as well as the confidence of users in B2B reputation systems

Improving the Accuracy of Business-to-Business (B2B) Reputation Systems through Rater Expertise Prediction

International audienceDigital ecosystems rely on reputation systems in order to build trust and to foster collaborations among users. Reputation systems are commonplace in the C2C and B2C contexts, however, they have not yet found mainstream acceptance in B2B environments. Our first contribution in this paper is to identify the particularities of feedback collection in B2B reputation systems. An issue that we identify is that the reputation target in the B2B context is a business, which requires evaluation on a large number of criteria. We observe that due to the wide variation in user expertise, feedback forms that require users to evaluate all criteria have significant negative consequences for rating accuracy. Our second contribution is to propose an expertise prediction algorithm for B2B reputation systems, which filters the criteria describing the target business such that each user rates only on those criteria that he has expertise in. Experiments based on our real dataset show that the algorithm accurately predicts the expertise of users in given criteria. The algorithm may also increase the motivation of users to submit feedback as well as the confidence of users in B2B reputation systems

Improving the accuracy of Business-to-Business (B2B) reputation systems through rater expertise prediction

International audienceDigital ecosystems rely on reputation systems in order to build trust and to foster collaborations among users. Reputation systems are commonplace in the C2C and B2C contexts, however, they have not yet found mainstream acceptance in B2B environments. Our first contribution in this paper is to identify the particularities of feedback collection in B2B reputation systems. An issue that we identify is that the reputation target in the B2B context is a business, which requires evaluation on a large number of criteria. We observe that due to the wide variation in user expertise, feedback forms that require users to evaluate all criteria have significant negative consequences for rating accuracy. Our second contribution is to propose an expertise prediction algorithm for B2B reputation systems, which filters the criteria describing the target business such that each user rates only on those criteria that he has expertise in. Experiments based on our real dataset show that the algorithm accurately predicts the expertise of users in given criteria. The algorithm may also increase the motivation of users to submit feedback as well as the confidence of users in B2B reputation systems

Partial Loss of USP9X Function Leads to a Male Neurodevelopmental and Behavioral Disorder Converging on Transforming Growth Factor β Signaling

The X-chromosome gene USP9X encodes a deubiquitylating enzyme that has been associated with neurodevelopmental disorders primarily in female subjects. USP9X escapes X inactivation, and in female subjects de novo heterozygous copy number loss or truncating mutations cause haploinsufficiency culminating in a recognizable syndrome with intellectual disability and signature brain and congenital abnormalities. In contrast, the involvement of USP9X in male neurodevelopmental disorders remains tentative. We used clinically recommended guidelines to collect and interrogate the pathogenicity of 44 USP9X variants associated with neurodevelopmental disorders in males. Functional studies in patient-derived cell lines and mice were used to determine mechanisms of pathology. Twelve missense variants showed strong evidence of pathogenicity. We define a characteristic phenotype of the central nervous system (white matter disturbances, thin corpus callosum, and widened ventricles); global delay with significant alteration of speech, language, and behavior; hypotonia; joint hypermobility; visual system defects; and other common congenital and dysmorphic features. Comparison of in silico and phenotypical features align additional variants of unknown significance with likely pathogenicity. In support of partial loss-of-function mechanisms, using patient-derived cell lines, we show loss of only specific USP9X substrates that regulate neurodevelopmental signaling pathways and a united defect in transforming growth factor β signaling. In addition, we find correlates of the male phenotype in Usp9x brain-specific knockout mice, and further resolve loss of hippocampal-dependent learning and memory. Our data demonstrate the involvement of USP9X variants in a distinctive neurodevelopmental and behavioral syndrome in male subjects and identify plausible mechanisms of pathogenesis centered on disrupted transforming growth factor β signaling and hippocampal function