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• W2888004646 abstract "Collaborative filtering algorithms find useful patterns in rating and consumption data and exploit these patterns to guide users to good items. Many of the patterns in rating datasets reflect important real-world differences between the various users and items in the data; other patterns may be irrelevant or possibly undesirable for social or ethical reasons, particularly if they reflect undesired discrimination, such as gender or ethnic discrimination in publishing. In this work, we examine the response of collaborative filtering recommender algorithms to the distribution of their input data with respect to a dimension of social concern, namely content creator gender. Using publicly-available book ratings data, we measure the distribution of the genders of the authors of books in user rating profiles and recommendation lists produced from this data. We find that common collaborative filtering algorithms differ in the gender distribution of their recommendation lists, and in the relationship of that output distribution to user profile distribution." @default.
• W2888004646 created "2018-08-31" @default.
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• W2888004646 date "2018-09-27" @default.
• W2888004646 modified "2023-10-01" @default.
• W2888004646 title "Exploring author gender in book rating and recommendation" @default.
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• W2888004646 doi "https://doi.org/10.1145/3240323.3240373" @default.
• W2888004646 hasPublicationYear "2018" @default.
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