Faceted Search is a widely used interaction scheme in digital libraries, e-commerce, and recently also in Linked Data. Surprisingly, object ranking in the context of Faceted Search is not well studied in the literature. In this article, we propose an extension of the model with two parameters that enable specifying the desired answer size and the granularity of the sought object ranking. These parameters allow tackling the problem of too big or too small answers and can specify how refined the sought ranking should be. Then, we provide an algorithm that takes as input these parameters and by considering the hard-constraints (filters), the soft-constraints (preferences), as well as the statistical properties of the dataset (through various frequency-based ranking schemes), produces an object ranking that satisfies these parameters, in a transparent way for the user. Then, we present extensive simulation-based evaluation results that provide evidence that the proposed model also improves the answers and reduces the user’s cost. Finally, we propose GUI extensions that are required and present an implementation of the model.

中文翻译：

---

具有对象排名和答案大小约束的分面搜索

分面搜索是数字图书馆、电子商务以及最近在关联数据中广泛使用的交互方案。令人惊讶的是，文献中没有很好地研究分面搜索上下文中的对象排名。在本文中，我们提出了模型的扩展，它具有两个参数，可以指定所需的答案大小和所寻找对象排名的粒度。这些参数可以解决以下问题太大要么太小答案并可以指定多么精致寻求的排名应该是。然后，我们提供一种算法，将这些参数作为输入，并通过考虑硬约束（过滤器）、软约束（偏好）以及数据集的统计特性（通过各种基于频率的排名方案），以对用户透明的方式生成满足这些参数的对象排名。然后，我们提出了广泛的基于模拟的评估结果，这些结果提供了证据表明所提出的模型还改进了答案并降低了用户的成本。最后，我们提出了所需的 GUI 扩展，并提供了模型的实现。