信息网络中的导航建模

D. Dimitrov
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The seminal information foraging theory has been developed suggesting that people follow links by constantly estimating their quality in terms of information value and cost associated with obtaining that value by interacting with the environment. Furthermore, models describing the network structure of the Web like the bow tie model, and the small world models have been introduced. These models contributed valuable insights towards characterizing the underlying network topology on which the users operate and the extent to which it allows efficient navigation. In the context of information networks, researchers have successfully modeled user navigation resorting to Markov chains and to decentralized search. With respect to the users' navigational behavior and their click activities to traverse a link, researchers have found a valuable source of information in the log files of Web servers. Click data has also been collected by letting humans play navigational games on Wikipedia. With this data, researchers tested different navigational hypotheses; for example, (i) if humans tend to navigate between semantically similar articles, (ii) if they experience a trade-off between following links leading towards semantically similar articles and following links leading towards possibly well-connected articles. For navigation with a particular target in mind, users are found to be greedy with respect to the next click if they are confident to be on the right path, whereas they tend to explore the information network at random if they feel insecure or lost and have no intuition about the next click. Although these research lines have advanced our understanding of navigational user behavior in information networks, for the goal of the proposed thesis-modeling navigation-related work does not address and cover the following questions: (i) What is the relationship between the user's awareness regarding the structure and the topology of the information network and the efficiency of navigation, i.e., modeled as decentralized search and (ii) How do users interact with the content to explore and discover it, i.e., are there some specific links that are especially appealing and what are their characteristics? My research focuses on modeling navigation in an information space represented as an information network. Regarding the first question, I introduce and apply partially informed decentralized search to model the extent to which a user is exposed to the network structure of the information space and can make informed decisions about her next step towards exploring the content [1]. I test different hypotheses regarding the type and the amount of network structural information used to model navigation. My results show that only a small amount of knowledge about the network structure is sufficient for efficient navigation. For the second question, I study large-scale click data from the English version of Wikipedia. I observe a focus of the users' attention towards specific links. With this part of the proposal, I want to shed light on a different aspect of navigation and concentrate on the question why some links are more successful than others. In particular, I study the relationship between the link properties and the link popularity as measured by transitional click data. To that end, I formulate navigational hypotheses based on different link features, i.e., network features, semantic features and visual features [2, 3]. The plausibility of these hypotheses is then tested using a Markov chain-based Bayesian hypothesis testing framework. Results suggest that Wikipedia users tend to select links located at the top of the page. Furthermore, users are tempted to select links leading towards the periphery of the Wikipedia network. To conclude, I believe that the won insights may have impact on system design decisions, i.e, existing guidelines for Wikipedia contributors can be adapted to better reflect the usage of the system.","PeriodicalId":344017,"journal":{"name":"Proceedings of the Tenth ACM International Conference on Web Search and Data Mining","volume":"103 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Modeling Navigation in Information Networks\",\"authors\":\"D. Dimitrov\",\"doi\":\"10.1145/3018661.3022754\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Navigation in an information space is a natural way to explore and discover its content. 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The seminal information foraging theory has been developed suggesting that people follow links by constantly estimating their quality in terms of information value and cost associated with obtaining that value by interacting with the environment. Furthermore, models describing the network structure of the Web like the bow tie model, and the small world models have been introduced. These models contributed valuable insights towards characterizing the underlying network topology on which the users operate and the extent to which it allows efficient navigation. In the context of information networks, researchers have successfully modeled user navigation resorting to Markov chains and to decentralized search. With respect to the users' navigational behavior and their click activities to traverse a link, researchers have found a valuable source of information in the log files of Web servers. Click data has also been collected by letting humans play navigational games on Wikipedia. With this data, researchers tested different navigational hypotheses; for example, (i) if humans tend to navigate between semantically similar articles, (ii) if they experience a trade-off between following links leading towards semantically similar articles and following links leading towards possibly well-connected articles. For navigation with a particular target in mind, users are found to be greedy with respect to the next click if they are confident to be on the right path, whereas they tend to explore the information network at random if they feel insecure or lost and have no intuition about the next click. Although these research lines have advanced our understanding of navigational user behavior in information networks, for the goal of the proposed thesis-modeling navigation-related work does not address and cover the following questions: (i) What is the relationship between the user's awareness regarding the structure and the topology of the information network and the efficiency of navigation, i.e., modeled as decentralized search and (ii) How do users interact with the content to explore and discover it, i.e., are there some specific links that are especially appealing and what are their characteristics? My research focuses on modeling navigation in an information space represented as an information network. Regarding the first question, I introduce and apply partially informed decentralized search to model the extent to which a user is exposed to the network structure of the information space and can make informed decisions about her next step towards exploring the content [1]. I test different hypotheses regarding the type and the amount of network structural information used to model navigation. My results show that only a small amount of knowledge about the network structure is sufficient for efficient navigation. For the second question, I study large-scale click data from the English version of Wikipedia. I observe a focus of the users' attention towards specific links. With this part of the proposal, I want to shed light on a different aspect of navigation and concentrate on the question why some links are more successful than others. In particular, I study the relationship between the link properties and the link popularity as measured by transitional click data. To that end, I formulate navigational hypotheses based on different link features, i.e., network features, semantic features and visual features [2, 3]. The plausibility of these hypotheses is then tested using a Markov chain-based Bayesian hypothesis testing framework. Results suggest that Wikipedia users tend to select links located at the top of the page. Furthermore, users are tempted to select links leading towards the periphery of the Wikipedia network. 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引用次数: 0

摘要

信息空间中的导航是探索和发现其内容的自然方式。像数字百科全书(例如,Wikipedia)这样的网络信息系统对向用户提供良好的导航支持很感兴趣。为此,导航模型对于估计信息空间的一般可导航性以及理解用户如何与之交互非常有用。这些模型还可以用于识别用户在导航过程中遇到的问题,并改进用户界面。研究网络导航是一项具有挑战性的任务,在我们的科学界有着悠久的传统。基于大量的研究,研究人员在理解Web上的导航用户行为方面取得了重大进展,确定了用户在导航过程中使用的一般使用模式、规律和策略。开创性的信息觅食理论已经发展起来,表明人们通过不断地根据信息价值和通过与环境互动获得该价值的相关成本来评估其质量,从而遵循链接。此外,还介绍了描述Web网络结构的模型,如领结模型和小世界模型。这些模型为描述用户操作的底层网络拓扑以及它允许有效导航的程度提供了有价值的见解。在信息网络的背景下,研究人员已经成功地利用马尔可夫链和分散搜索对用户导航进行了建模。关于用户的导航行为和浏览链接的点击活动,研究人员在Web服务器的日志文件中发现了一个有价值的信息源。点击数据也通过让人们在维基百科上玩导航游戏来收集。有了这些数据,研究人员测试了不同的导航假设;例如,(i)如果人们倾向于在语义相似的文章之间导航,(ii)如果他们在点击链接指向语义相似的文章和点击链接指向可能关联良好的文章之间进行权衡。对于有特定目标的导航,如果用户有信心在正确的路径上,他们会对下一次点击感到贪婪,而如果他们感到不安全或迷路,并且对下一次点击没有直觉,他们倾向于随机探索信息网络。尽管这些研究方向促进了我们对信息网络中导航用户行为的理解,但对于本文提出的目标,建模与导航相关的工作并没有解决和涵盖以下问题:(i)用户对信息网络结构和拓扑的认知与导航效率之间的关系是什么,即以分散搜索为模型;(ii)用户如何与内容交互以探索和发现内容,即是否有一些特别吸引人的特定链接,它们的特征是什么?我的研究集中在建模导航的信息空间表示为一个信息网络。关于第一个问题,我引入并应用了部分知情的分散搜索来模拟用户暴露于信息空间的网络结构的程度,并可以对探索内容[1]的下一步做出明智的决定。我测试了关于用于建模导航的网络结构信息的类型和数量的不同假设。我的研究结果表明,只有少量的网络结构知识就足以进行有效的导航。对于第二个问题,我研究了英文版维基百科的大规模点击数据。我观察到用户的注意力集中在特定链接上。在提案的这一部分,我想阐明导航的另一个方面,并集中讨论为什么有些链接比其他链接更成功。特别地,我研究了链接属性和链接流行度之间的关系,这是由过渡点击数据衡量的。为此,我根据不同的链接特征,即网络特征、语义特征和视觉特征,制定了导航假设[2,3]。然后使用基于马尔可夫链的贝叶斯假设检验框架测试这些假设的合理性。结果表明,维基百科用户倾向于选择位于页面顶部的链接。此外,用户很容易选择指向维基百科外围网络的链接。总而言之,我相信这些见解可能会对系统设计决策产生影响,也就是说,现有的维基百科贡献者指南可以被修改,以更好地反映系统的使用情况。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modeling Navigation in Information Networks
Navigation in an information space is a natural way to explore and discover its content. Information systems on the Web like digital encyclopedias (e.g., Wikipedia) are interested in providing good navigational support to their users. To that end, navigation models can be useful for estimating the general navigability of an information space and for understanding how users interact with it. Such models can also be applied to identify problems faced by the users during navigation and to improve user interfaces. Studying navigation on the Web is a challenging task that has a long tradition in our scientific community. Based on large studies, researchers have made significant steps towards understanding navigational user behavior on the Web identifying general usage patterns, regularities, and strategies users apply during navigation. The seminal information foraging theory has been developed suggesting that people follow links by constantly estimating their quality in terms of information value and cost associated with obtaining that value by interacting with the environment. Furthermore, models describing the network structure of the Web like the bow tie model, and the small world models have been introduced. These models contributed valuable insights towards characterizing the underlying network topology on which the users operate and the extent to which it allows efficient navigation. In the context of information networks, researchers have successfully modeled user navigation resorting to Markov chains and to decentralized search. With respect to the users' navigational behavior and their click activities to traverse a link, researchers have found a valuable source of information in the log files of Web servers. Click data has also been collected by letting humans play navigational games on Wikipedia. With this data, researchers tested different navigational hypotheses; for example, (i) if humans tend to navigate between semantically similar articles, (ii) if they experience a trade-off between following links leading towards semantically similar articles and following links leading towards possibly well-connected articles. For navigation with a particular target in mind, users are found to be greedy with respect to the next click if they are confident to be on the right path, whereas they tend to explore the information network at random if they feel insecure or lost and have no intuition about the next click. Although these research lines have advanced our understanding of navigational user behavior in information networks, for the goal of the proposed thesis-modeling navigation-related work does not address and cover the following questions: (i) What is the relationship between the user's awareness regarding the structure and the topology of the information network and the efficiency of navigation, i.e., modeled as decentralized search and (ii) How do users interact with the content to explore and discover it, i.e., are there some specific links that are especially appealing and what are their characteristics? My research focuses on modeling navigation in an information space represented as an information network. Regarding the first question, I introduce and apply partially informed decentralized search to model the extent to which a user is exposed to the network structure of the information space and can make informed decisions about her next step towards exploring the content [1]. I test different hypotheses regarding the type and the amount of network structural information used to model navigation. My results show that only a small amount of knowledge about the network structure is sufficient for efficient navigation. For the second question, I study large-scale click data from the English version of Wikipedia. I observe a focus of the users' attention towards specific links. With this part of the proposal, I want to shed light on a different aspect of navigation and concentrate on the question why some links are more successful than others. In particular, I study the relationship between the link properties and the link popularity as measured by transitional click data. To that end, I formulate navigational hypotheses based on different link features, i.e., network features, semantic features and visual features [2, 3]. The plausibility of these hypotheses is then tested using a Markov chain-based Bayesian hypothesis testing framework. Results suggest that Wikipedia users tend to select links located at the top of the page. Furthermore, users are tempted to select links leading towards the periphery of the Wikipedia network. To conclude, I believe that the won insights may have impact on system design decisions, i.e, existing guidelines for Wikipedia contributors can be adapted to better reflect the usage of the system.
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