Deep Learning of Human Information Foraging Behavior with a Search Engine
Deep Learning of Human Information Foraging Behavior with a Search Engine
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DOI：
10.1145/3341981.3344231
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发表时间：
2019-09
期刊：
影响因子：
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通讯作者：
Xi Niu;Xiangyu Fan
中科院分区：
文献类型：
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作者：
Xi Niu;Xiangyu Fan
本文提出了一个两级深度学习框架来模拟人类在搜索引擎中的信息获取行为。设计了一种以LSTM为基本单元的递归神经网络结构，以显式地考虑信息气味的时间和空间相关性，这是信息觅食理论中的关键概念。目标是预测几种主要的搜索行为，如查询放弃、查询重组、点击次数和信息收益。LSTM的记忆能力和序列结构不仅可以自然地模拟用户目前正在感知和执行的内容，还可以模拟他们在搜索动态过程中从过去看到和学习的内容。实验结果表明，与最新的神经点击模型相比，不同输入变量的信息气味模型对某些搜索行为的预测效果更好。当将来自先前查询的知识合并到同一搜索会话中时，对当前查询丢弃、分页和信息获取的预测得到了改进。与众所周知的在单个搜索查询线程下模拟搜索行为的神经点击模型相比，本研究以更广泛的视角来考虑可能包含多个查询的整个搜索会话。更重要的是，我们的模型将搜索引擎结果页面上的搜索结果相关性模式作为一个整体作为信息气味输入到深度学习模型中，而不是每一步考虑一个搜索结果。这一结果对信息气味对人们如何寻找信息的影响有了深刻的见解，这对设计或完善一套搜索引擎设计指南具有重要意义。
In this paper, a two-level deep learning framework is presented to model human information foraging behavior with search engines. A recurrent neural network architecture is designed using LSTM as the base unit to explicitly consider the temporal and spatial dependencies of information scents, the key concept in Information Foraging Theory. The target is to predict several major search behaviors, such as query abandonment, query reformulation, number of clicks, and information gain. The memory capability and the sequence structure of LSTM allow to naturally mimic not only what users are perceiving and performing at the moment but also what they have seen and learned from the past during the search dynamics. The promising results indicate that our information scent models with different input variations were better, compared to the state-of-the art neural click models, at predicting some search behaviors. When incorporating the knowledge from a previous query in the same search session, the prediction of current query abandonment, pagination, and information gain has been improved. Compared to the well known neural click models that model search behaviors under a single search query thread, this study takes a broader view to consider an entire search session which may contain multiple queries. More importantly, our model takes the search result relevance pattern on the Search Engine Results Pages (SERP) as a whole as the information scent input to the deep learning model, instead of considering one search result at each step. The results have insights on the impact of information scents on how people forage for information, which has implications for designing or refining a set of design guidelines for search engines.