论文标题
预测造物主的偏好和互动生成艺术
Predicting A Creator's Preferences In, and From, Interactive Generative Art
论文作者
论文摘要
当外行用户使用交互式生成艺术工具创建艺术品时，他们做出的选择会告诉我们有关它们及其偏好的选择？这些偏好可以以特定的生成艺术形式（例如，调色板，零件的密度，碎片的厚度或曲率）；预测它们可能会导致更智能的交互式工具。或者它们可能是其他各行各业（例如音乐，时尚，食物，室内设计，绘画）或人属性（例如人格类型，性别，性别，艺术倾向）的偏好；预测它们可能会改善产品或体验的个性化建议。 为了研究这一研究问题，我们以特定的生成艺术形式和其他各行各业中收集了311位受试者的偏好。我们分析了偏好和训练机器学习模型，以预测剩余的偏好子集。我们发现，我们研究的生成艺术形式的偏好不能比机会更好地预测其他各行各业的偏好（反之亦然）。但是，生成艺术形式中的偏好彼此可靠地预测。
As a lay user creates an art piece using an interactive generative art tool, what, if anything, do the choices they make tell us about them and their preferences? These preferences could be in the specific generative art form (e.g., color palettes, density of the piece, thickness or curvatures of any lines in the piece); predicting them could lead to a smarter interactive tool. Or they could be preferences in other walks of life (e.g., music, fashion, food, interior design, paintings) or attributes of the person (e.g., personality type, gender, artistic inclinations); predicting them could lead to improved personalized recommendations for products or experiences. To study this research question, we collect preferences from 311 subjects, both in a specific generative art form and in other walks of life. We analyze the preferences and train machine learning models to predict a subset of preferences from the remaining. We find that preferences in the generative art form we studied cannot predict preferences in other walks of life better than chance (and vice versa). However, preferences within the generative art form are reliably predictive of each other.
