建模和预测信息搜索行为

Saraschandra Karanam, H. Oostendorp, M. Sanchiz, A. Chevalier, Jessie Chin, W. Fu
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引用次数: 11

摘要

本文着眼于网络导航认知模型的两个局限性:第一,它们没有考虑到信息搜索的整个过程;第二,它们没有考虑到年龄导致的搜索行为差异。为了解决这些限制,我们使用了一个实验的数据,在这个实验中,两种类型的信息搜索任务(简单和困难)分别呈现给年轻和年老的参与者。我们发现,一般来说,与简单任务相比,困难任务需要更多的时间,更多的点击,更多的重新表述,而且回答的准确性要低得多。年龄较大的人检查搜索引擎结果页面的时间明显更长,对困难任务的重新表述明显少于年轻人,对简单任务的重新表述明显比年轻人更准确。接下来,我们使用了一个名为CoLiDeS的网络导航认知模型来预测用户会选择点击哪个搜索引擎结果。研究发现,年长的参与者只会在搜索引擎中与查询内容语义相似度高的搜索结果上点击更多。老年参与者生成的搜索引擎结果的语义相似度值(与查询一起计算)仅在第二个周期中高于年轻参与者生成的搜索引擎结果。模型预测的点击次数与实际用户点击次数之间的匹配度在困难任务中明显高于简单任务。讨论了增强建模及其应用的潜在改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modeling and predicting information search behavior
This paper looks at two limitations of cognitive models of web-navigation: first, they do not account for the entire process of information search and second, they do not account for the differences in search behavior caused by aging. To address these limitations, data from an experiment in which two types of information search tasks (simple and difficult), presented to both young and old participants was used. We found that in general difficult tasks demand significantly more time, significantly more clicks, significantly more reformulations and are answered significantly less accurately than simple tasks. Older persons inspect the search engine result pages significantly longer, produce significantly fewer reformulations with difficult tasks than younger persons, and are significantly more accurate than younger persons with simple tasks. We next used a cognitive model of web-navigation called CoLiDeS to predict which search engine result a user would choose to click. Old participants were found to click more often only on search engine results with high semantic similarity with the query. Search engine results generated by old participants were of higher semantic similarity value (computed w.r.t the query) than those generated by young participants only in the second cycle. Match between model-predicted clicks and actual user clicks was found to be significantly higher for difficult tasks compared to simple tasks. Potential improvements in enhancing the modeling and its applications are discussed.
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