Date: March 2007
Creator: Liu, Hugo & Mihalcea, Rada, 1974-
Description: This paper discusses data-driven gender modeling for improved user interfaces. Abstract: Men and women have unique sensibilities for information, which can be tapped to create gender-sensitive user interfaces that appeal more specifically to each sex. Building on previous research in gender psychology and also in user modeling, the authors take a data-driven approach to understanding gender preferences by mining a large corpus of 150,000 weblog entries - half authored by men, half by women. This paper reports two kinds of contributions. First, the authors employ automatic language processing, semantic analysis, and reflexive ethnography to articulate gender preferences for several dimensions of gender space will provide valuable insight to user interface designers- time, color, size, socialness, affect, and cravings. Second, the authors employ statistical gender models to build GenderLens- a novel intelligent news filtering system that customizes news based on the gender of its reader. A user evaluation found that GenderLens successfully predicted men and women's preferences for news, with statistical significance for four out of five news genres tested.
Contributing Partner: UNT College of Engineering