Computational models of information scent-following in a very large browsable text collection
Computational models of information scent-following in a very large browsable text collection
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非常大的可浏览文本集合中信息气味跟踪的计算模型
DOI：
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发表时间：
1997
期刊：
影响因子：
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通讯作者：
P. Pirolli
中科院分区：
文献类型：
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作者：
P. Pirolli
在分析中介绍并使用了分析的生态认知fimnework和模型跟踪amhitecture，并将其用于分析CF数据记录的FLOM用户浏览大量文档收集。 USEM与散点/收集浏览器进行了互动，该浏览器将文档群分为相似的内容，并向用户呈现群集内容的摘要。计算模型D导航和信息觅食的预测与观察到的活动相匹配。
An ecological-cognitive fimnework of analysis and a modeltracing amhitecture are presented and used in the analysis cf data recorded flom users browsing a large document collection. The usem interacted with the Scatter/Gather browser, which clusters documents into groups of similar content and presents users with summaries of cluster content. Predictions made by a computational model d navigation and information foraging are matched against the observed activity.