论文标题
一种基于本地约束的图形匹配的策划艺术展览的股票感知的推荐系统
An Equity-Aware Recommender System for Curating Art Exhibits Based on Locally-Constrained Graph Matching
论文作者
论文摘要
公共艺术塑造了我们共享的空间。公共艺术应该与社区和背景交谈，但是最近的工作表明，在著名的机构中，有许多艺术实例，这些机构赞成过时的文化规范和传统社区。在此激励的情况下，我们开发了一种新颖的推荐系统，以策划具有内置权益目标和基于本地价值的约束资源的分配的公共艺术展览。我们通过利用Schelling的隔离模型来开发成本矩阵。使用成本矩阵作为输入，通过投影梯度下降来优化评分函数以获得软分配矩阵。我们的优化计划通过满足最低代表和暴露标准，以更优先降低“组内”偏好的方式将艺术品分配给公共场所。我们利用现有文献来为我们的算法输出开发公平度量，并评估方法的有效性，并从策展和公平的角度讨论其潜在的陷阱。
Public art shapes our shared spaces. Public art should speak to community and context, and yet, recent work has demonstrated numerous instances of art in prominent institutions favoring outdated cultural norms and legacy communities. Motivated by this, we develop a novel recommender system to curate public art exhibits with built-in equity objectives and a local value-based allocation of constrained resources. We develop a cost matrix by drawing on Schelling's model of segregation. Using the cost matrix as an input, the scoring function is optimized via a projected gradient descent to obtain a soft assignment matrix. Our optimization program allocates artwork to public spaces in a way that de-prioritizes "in-group" preferences, by satisfying minimum representation and exposure criteria. We draw on existing literature to develop a fairness metric for our algorithmic output, and we assess the effectiveness of our approach and discuss its potential pitfalls from both a curatorial and equity standpoint.
