搜索、学习和子主题排序：基于模拟的分析
Searching, Learning, and Subtopic Ordering: A Simulation-based Analysis
摘要：
复杂的搜索任务-例如来自“搜索即学习”（SAL）领域的搜索任务-常常导致用户产生由几个方面组成的信息需求。然而，目前的行为模型假设个人有一个原子的需要，无论任务。虽然这些模型一般都能很好地满足简单的信息需求，但我们认为，需要进一步开发可扩展模型，以便将复杂的搜索任务分解为多个方面。由于还没有一个模型，认为这两个方面和SAL域，我们建议，通过增加复杂的搜索模型（CSM），子主题意识复杂的搜索模型（SACSM）建模方面的子主题，以用户的需要。然后，我们实例化几个代理（即，模拟用户），具有不同的子主题选择策略，其可以被认为是不同的原型学习策略（例如，我应该一次深入研究一个子主题，还是肤浅地涵盖几个子主题？）最后，我们报告的第一个大规模的模拟分析用户行为在SAL域。结果表明，SACSM，在一定的条件下，准确地模拟用户的行为。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
