论文标题
搜索，学习和亚主题订购：基于仿真的分析
Searching, Learning, and Subtopic Ordering: A Simulation-based Analysis
论文作者
论文摘要
复杂的搜索任务（例如从学习（SAL）域）的搜索任务通常会导致用户开发由几个方面组成的信息。但是，当前的搜索者行为模型假定个人有原子需求，而不论任务如何。尽管这些模型通常可以很好地满足更简单的信息需求，但我们认为需要进一步开发搜索器模型，以使复杂的搜索任务分解为多个方面。由于尚无搜索器模型存在着考虑方面和SAL域的考虑，因此，我们通过增强复杂的搜索器模型（CSM）来提出，亚电头知识的复杂搜索器模型（SACSM） - 将方面作为亚电位对用户的需求进行建模。然后，我们实例化了几种代理（即模拟用户），并具有不同的亚主题选择策略，可以将其视为不同的原型学习策略（例如，我一次一次对一个亚主题进行深入研究，或者涵盖了几个小主题？）。最后，我们报告了SAL域中用户行为的第一个大规模模拟分析。结果表明，在某些条件下，SACSM可以准确模拟用户行为。
Complex search tasks - such as those from the Search as Learning (SAL) domain - often result in users developing an information need composed of several aspects. However, current models of searcher behaviour assume that individuals have an atomic need, regardless of the task. While these models generally work well for simpler informational needs, we argue that searcher models need to be developed further to allow for the decomposition of a complex search task into multiple aspects. As no searcher model yet exists that considers both aspects and the SAL domain, we propose, by augmenting the Complex Searcher Model (CSM), the Subtopic Aware Complex Searcher Model (SACSM) - modelling aspects as subtopics to the user's need. We then instantiate several agents (i.e., simulated users), with different subtopic selection strategies, which can be considered as different prototypical learning strategies (e.g., should I deeply examine one subtopic at a time, or shallowly cover several subtopics?). Finally, we report on the first large-scale simulated analysis of user behaviours in the SAL domain. Results demonstrate that the SACSM, under certain conditions, simulates user behaviours accurately.
