Social navigation

Last updated

Social navigation is a form of social computing introduced by Paul Dourish and Matthew Chalmers in 1994, who defined it as when "movement from one item to another is provoked as an artifact of the activity of another or a group of others". [1] According to later research in 2002, "social navigation exploits the knowledge and experience of peer users of information resources" to guide users in the information space, and that it is becoming more difficult to navigate and search efficiently with all the digital information available from the World Wide Web and other sources. [2] Studying others' navigational trails and understanding their behavior can help improve one's own search strategy by guiding them to make more informed decisions based on the actions of others. [3]

Contents

Prior to the advancement of Web 2.0 and the Social Web, the World Wide Web had been a solitary space where users were unaware of where anyone else was browsing or navigating. [4] The scope of social navigation research has been increasing, especially as information visualization improves. Displaying social information in virtual spaces allows user behavior models to make digital systems feel more social and less solitary. [2]

Supporting theories and techniques

The concept of social navigation is supported by several theories. Information foraging theory studies human behavior when they are seeking, gathering, sharing and consuming information. [5] It applies optimal foraging theory to human behavior when they navigate to information, [6] and explains how people benefit from other people based on history-rich digital objects, which explains the idea of used items or paths. For example, a used book that has notes, highlights and underlines is different from a new book. History-rich digital objects help people find the target faster and more efficiently. [7]

Information foraging is an alternative to food foraging and ant colony optimization, [6] which state that information human-hunters follow others’ paths to optimally reach their target. Optimal information must maximize the value of the information that is gained per unit cost (like time or effort). [5] This theory supports collaborative activities, [8] and is a guide for designers to build good interfaces where users can benefit from others' research. [7]

The weaknesses of this theory are when people mistrace information; they cannot be redirected unless they figure it out, [7] and optimization is not always the case for human behavior [8]

The information patch model studies time spent in navigation in filtered information and clustered information, and works to optimize the overall information as fast as possible; [5] [8] the information scent model determines information value by taking the most useful cues that have been used by other users; [5] [8] and the information diet model (prey selection) explains how people select the target information based on others' selections, which leads to optimal satisfying information. [5] [8]

Webpage design is also important in how a user interacts with the internet in a social manner. There is a correlation between accessibility and popularity: [9] the more functional a website is, the more traffic it will receive. [9] A more frequented web service will naturally be a more social experience. There are numerous factors that contribute to accessibility, such as a page's location, properties, number of hyperlinks, and modes of access. [10] [11] As every person has a different approach to surfing the web, internet navigation is defined as "[t]he creation and interpretation of an internal (mental) model, and its component activities are browsing, modelling, interpretation and formulation of browsing strategy." [10] There is a theory that if a user calibrates their browsing strategy to reflect their interests, pages relevant to their interests will be found more easily. [10] Uninformed navigation through hyperlinks can be misleading and result in a higher number of unwanted sites being accessed. [12] Bookmarking is a method to return to sites that appeal to one's interests, [12] as it creates a hyperlink that is saved for future browsing. When a population bookmarks the same page and visits it frequently, it forms a sense of community. Recently, live updating of other current users adds another dimension to the social aspect of web browsing. [12]

Collaborative filtering is another technique that is prevalent and utilized in social navigation. It suggests that if users were presented search results based on traffic by others who share similar social interests, it would result in a more rewarding and efficient experience. [13] For example, Amazon.com has a "Customers Who Bought This Item Also Bought" feature that presents shoppers with other products bought by similar users, which streamlines the flow of web browsing and facilitates access to more relevant pages. [13]

Social navigation can also be discussed in different types of virtual worlds. Munro shares some original conceptions when considering social navigation. Instead of individual interaction, it can be presented as "a way of moving through an information space and exploiting the activities and orientations of others in that space as a way of managing one's spatial activities". Munro points out that spatial navigation, which mostly depends on the structure itself, like landscape or map, can be contrasted with social navigation. User can not only with the data and objects in a specific space, but also other individuals and their interactions. [14]

Traces of users' activities

As users navigate through online communities they leave traces of their activities, both intentional and unintentional. Intentional traces include posts, responses to other users’ posts, number of friends, uploaded media, and other activities where users intentionally share information. Unintentional traces include browsing history, times spent on particular pages, bounce rates, and other activities where users’ actions are automatically logged by web servers into server logs.

Björneborn categorizes online community users as “trace leavers” (i.e. users who leave actionable items) and "trace finders" (i.e. users who follow traces left by trace leavers). These participatory activities can guide other users’ information seeking behavior and influences features of social search and social navigation. [15] Combining trace-leaving activities of social browsing with the concept of social searching relies on recording and reusing focused search activities of like-minded searchers to produce search results that are better suited to the needs of a particular online community, as demonstrated by Freyne et al. [16]

Websites such as Amazon.com analyze user traces, such as history of purchases or product reviews, to generate recommendations for other users (e.g. "Customers Who Bought This Item Also Bought..."). [17] Online platforms for collaborative software development such as GitHub rely on activity traces, such as the number of repositories, history of activity across projects, commits, and personal profiles to determine its users' reputations in the community. [18]

User activity traces can be used to model users’ behavioral patterns and trends to determine the health of online communities (whether a community would flourish or diminish). [19] Such models can also be used to predict propagation and future popularity of content, [20] or predict results before voting occurs. [21] Activity and traffic patterns can be used to evaluate the performance of existing systems, and improve site usability, architecture, and infrastructure. [22]

Tag-based social navigation

There are primarily two strategies to explore and discover an information space: the first one is the regular search, where users are aware of what they are searching for. Under this context, users have a target information in mind. They usually need to formulate a search query first before inputting it into a search engine; another search strategy is navigation, where users do not have a target information in mind but rather explore through pieces of information by following certain hyperlinks.

Navigation is considered to have advantages over searching, since recognizing what users are looking for is easier than formulating and describing the information people need, which refers to the "vocabulary problem". [23] Social tagging serves as a new social way of organizing a set of resources, and approaches the "vocabulary problem" from a new social angle. Social tagging systems allow people to annotate a set of resources according to their own needs with freely chosen words—tags, and share them with other users of the social tagging system. The result of this human-based annotation of resources is called folksonomy. Examples of social tagging systems are BibSonomy, CiteULike, Flickr, and Delicious.

Tag cloud

A tag cloud is a textual representation of the topic or subject collectively seen by the users and it captures the "aboutness" of the resource.

Tag clouds are easy to build, intuitive to understand, and widely used. It can also represent the three types of relationship among users, tags, and resources in the tagging systems. However, there is a size limitation on the tag cloud that can be presented in the screen; selecting the best tags and structuring the information space to present the relationships in the tag cloud is important.[ citation needed ]

Tag clouds are very simple, and can be applied to support the user. Researchers find that tag cloud is usually more useful for the following four different tasks, as illustrated by Rivadeneira et al.: [24]

Researchers also found that different layouts are useful when performing different tasks. They also demonstrated that tag cloud typography (font size/position) matters: font size has a bigger impact on finding a tag than other visual features like color, tag string length, and tag location. [25] [26] [27]

Based on previous research, common ways to perform tag cloud evaluation are: [28] [29]

Tag clustering

An issue with social tagging data is the lack of structure. Synonymy, polysemy and homonymy or problems regarding tag semantics are additional issues related to tagging data. There are two main categories: flat and hierarchical clustering algorithms.

Flat classification can refer to three methods: content-based method, which is a widely-adopted algorithm for tag cloud selection is TopN algorithm proposed by Venetis et al.; [29] network-based method, which splits a graph of connected tags into clusters; and machine learning method, where the semantic relationship between tags is considered.

Hierarchical tag clustering refers to the creation of a hierarchical structure out of unstructured tagging data. The structure can be seen as the users’ mental maps of the information space, and can be used as a navigational aid. Hierarchical tag clustering can refer to three methods:

Modeling navigation in social tagging systems

Modeling tag-based navigation is used to understand the processes occurring in a social tagging system and how the system is used. There are two factors to understand modeling tag-based navigation in social tagging systems: basic modeling framework for navigation and theories understanding of the ability of folksonomies to guide navigation.

Basic modeling framework for navigation

Markov chain models:

  • Navigation on the Web can be seen as the process of following links between web pages
  • Markov chain models assign transition probabilities between web pages (also called states)
  • First order Markov chains (the transition probability between states depends only on the current state) are more commonly used

Decentralized search:

  • Navigation in a network can be modeled by the message-passing algorithm decentralized search
  • The message holder passes a message to one of its immediate neighbor nodes until the target node is found
  • At each step, the decision of movement is only made by the local knowledge of the network
  • Finding a path to a node (already realized in web navigation)

Different scholars provided the theoretic support to argue the suitability of folksonomies as a navigational aid. There are four main perspectives:

  • Network theoretic perspective has two aspects: the general navigability of a folksonomy as a graph, or the ability of tag hierarchies to guide navigation in such a graph
  • Information theoretic perspective suggest to see social tagging as the collective effort of creating a mental map that summarize an information space
  • Information foraging perspective to describe the human information seeking in a digital environment
  • Tagging vs. library approach. They[ who? ] proposed a definition of a controlled vocabulary and compared unrestricted free-form vocabularies emerged in social tagging systems to controlled vocabularies

Pragmatic folksonomy evaluation

The evaluation method introduced in this section is based on the paper by Helic et al. [30] The author proposed in the paper the general idea that people can leverage on the output produced by folksonomy algorithms (hierarchical structures) as input (background knowledge) for decentralized search for the following reasons:

  • The performance of decentralized search highly depends on the quality of the hierarchical clustering results that developed to facilitated navigation.
  • The performance of the decentralized search algorithm depends on the suitability of folksonomies.
  • The authors proposed the simulation method on decentralized search can be leveraged to evaluate the suitability of folksonomies.

Implementation examples

Educational systems

Various applications of social navigation have been studied in educational system, such as Knowledge Sea II. Compared to traditional approaches (Closed Corpus), it is able to gather online information (named Open Corpus) and feedback from different sources. Group traffic is used as feedback to indicate social navigation information such as "the most important parts of the textbooks". After a classroom study, Knowledge Sea II system shows better performance in visualization of content relevance of the textbook and satisfaction of student users. [31]

Mertens and his colleagues optimized the pre-existing system, virtPresenter, with the addition of hypermedia navigation concept. bookmarks, footprints and structural elements are integrated to help users to access lecture recordings and support social navigation for future users as well. The new version of virtPresenter shows better performance in social navigation function such as visualization, week-based filtering, and exchangeable bookmarks. [32]

Farzan and Brusilovsky introduced the AnnotatEd system, which combines functions of web annotation and adaptive navigation support to synergize social navigation application in web-based education. With implementations of web annotation and social navigation support, the system integrated Knowledge Sea II, and has been evaluated for six semesters in School of Information Systems at the University of Pittsburgh, which shows the significantly higher positive user attitude towards the new system because of its social navigation integration. [33]

Urban mobile information system

A system called CityFlocks was introduced to show social navigation implementation in an urban mobile information system. [34] The implementation is described by Bilandzic et al. (2008). [35] To solve the “socially blind” problem based on the influx of mobile phone users, CityFlocks was designed to enable web annotations combined with coordinates upon physical targets in the city directly or indirectly. Focus groups were chosen to collect requirements and problems in social navigation. The system is designed and generated by using appropriate techniques such as Google Maps and information retrieval. User tests of CityFlocks indicated that an indirect approach is more acceptable than a direct one.[ citation needed ]

Prototypes

Two prototypes of social navigation systems have been introduced: Juggler and Vortex. The Juggler system combines MOO[ clarification needed ], a textual virtual environment, and a Web client. The Vortex system uses a simplified desktop to present URLs. [36]

History-enriched implementation

History-enriched implementation of social navigation is based on the making the traces of behavior of latent users visible to future users. The implementation of the idea can be attributed to Wexelblat and Maes, who introduced an information space enriched with various social navigation mechanisms: document map, navigation paths, and documents' annotations and signposts. [37] They used six properties: Proxemic versus Distemic, Active versus Passive, Rate of Change, Degree of Permeation, Personal versus Social, and Kind of Information. More examples of history-rich information spaces has been implemented in different context such as educational domain, [38] location-based networking, and food recipes.

Social Navigation Network (SoNavNet), a location-based social network application devised by Hassan Karimi and his team, is aimed at sharing navigation experience. Other than simply showing the shortest time or distance like Google Maps, users’ specific experience and recommendation are underlined. With both geo-position and message functions, SoNavNet allows users to send requests to their friends while presenting their current location and points of interest, from which they acquire route and venue information oriented to their needs. [39]

Martin Svensson and his team created a recipe recommendation system, European Food On-Line, which has both direct and indirect navigation approaches. Social Navigator was implemented as a Java servlet to model users' behavior and net-based communication usages. [40]

Embedded visualization implementation

Social navigation implementation plays a significant role in guiding users to find information they need. Wesley Willett and his team designed Scented Widgets, which improves navigation with embedded visualization. They implemented scent metrics with a standard interface widget and used visual encoding for data. Hue, saturation, opacity, text, icon, bar chart, and line chart are scent encodings to highlight various information, which can display different types of data at the same time. They used Java Swing and the platform's pluggable look and feel to create and change widgets at runtime. In order to design a user-friendly interface, they followed scent encoding, layout, and composition guidelines. [41]

Implementation in usable security

In a file sharing system, every user can determine which files in their own computer can be shared through the network. Initially, users had to configure these security-related settings by themselves, and about eight out of ten users would unintentionally leak their private information such as credit card information or their address. Based on this problem, Paul DiGIoia and Paul Dourish from University of California, Irvine, introduced a pile metaphor model that used social navigation to solve the issue.

The pile metaphor model focuses on two major parts. First, users can be shown how other users in this system decide which files are shared, and such information is shown directly as folders; different folder appearances indicate different sharing levels. Based on this straightforward design, users can easily know that whether their decision is appropriate or not. Secondly, the pile metaphor model also shows the number of users in the system that have read a file by showing the tidiness of the pile. For example, the more times a file is read, the messier the pile is.

The pile metaphor model has two advantages: introducing this model to a system does not change the fundamental design of the system, as it is like a small plug-in and will have significant influence on the users; and it does not detract users from their work, because every security-related feature is shown directly on the graphical user interface. [42]

Implementation in human-robot interaction

One of the common methods people used in the field of social navigation is to construct proxemics, which can be connected with human-robot interaction. A study shows interests in different kinds of navigation behaviors humans expect from a robot in a path crossing scenario. The result reveals that spatial relationship actually relates to the behavior, which leads to a possible prediction to the expected action. [43]

Drawbacks of social navigation

Social navigation can be abused by malicious users who intend to mislead the public or obtain private information about specific person.

Researchers Meital Ben Sinai, Nimrod Partush, Shir Yadid, and Eran Yahav from Israel Technion performed some experiments in 2014 and wrote an article, “Exploiting Social Navigation”, to discuss the results. According to the article, attackers can use multiple machines to fake users’ behavior and fabricate information to mislead other real users. In this case, they attacked a real-time traffic software that allows users to report traffic news, and broadcast these messages to others. The researchers used fake users to fabricate traffic information like obstruction or traffic jams, and successfully let the system mislead real users. Real users could waste time and money to go a different route, or lead them onto unsafe non-existent routes, which cause security related issues. To solve this problem, social navigation systems sometimes verify the users’ identities through verification codes.

The verification technique can lead to another problem of social navigation: information leakage. Sinai et al. discussed that malicious attackers may exploit user information to gain private information, which causes security-related issues, since attackers can use the information to track other people with malicious intent. [44]

As the popularity of social networks and social web grows, data can be collected through the footprints of users left behind as they interact within different social computing systems. The growth has led to more novel and diverse implementation of social navigation support, including in education, media, news, and tour guide systems. Social navigation implementation in shared 3D environment works similarly, as it allows users to see trail and information of others who were in the same place before in the virtual world. [45] Bosch improved real navigation systems for driving and used social navigation to reduce driving times. [46]

See also

Related Research Articles

<span class="mw-page-title-main">Semantic Web</span> Extension of the Web to facilitate data exchange

The Semantic Web, sometimes known as Web 3.0, is an extension of the World Wide Web through standards set by the World Wide Web Consortium (W3C). The goal of the Semantic Web is to make Internet data machine-readable.

Personal information management (PIM) is the study and implementation of the activities that people perform in order to acquire or create, store, organize, maintain, retrieve, and use informational items such as documents, web pages, and email messages for everyday use to complete tasks and fulfill a person's various roles ; it is information management with intrapersonal scope. Personal knowledge management is by some definitions a subdomain.

Information foraging is a theory that applies the ideas from optimal foraging theory to understand how human users search for information. The theory is based on the assumption that, when searching for information, humans use "built-in" foraging mechanisms that evolved to help our animal ancestors find food. Importantly, a better understanding of human search behavior can improve the usability of websites or any other user interface.

<span class="mw-page-title-main">Collaborative filtering</span> Algorithm

Collaborative filtering (CF) is a technique used by recommender systems. Collaborative filtering has two senses, a narrow one and a more general one.

A recommender system, or a recommendation system, is a subclass of information filtering system that provides suggestions for items that are most pertinent to a particular user. Recommender systems are particularly useful when an individual needs to choose an item from a potentially overwhelming number of items that a service may offer.

Social bookmarking is an online service which allows users to add, annotate, edit, and share bookmarks of web documents. Many online bookmark management services have launched since 1996; Delicious, founded in 2003, popularized the terms "social bookmarking" and "tagging". Tagging is a significant feature of social bookmarking systems, allowing users to organize their bookmarks and develop shared vocabularies known as folksonomies.

<span class="mw-page-title-main">Thomas Vander Wal</span> American information architect

Thomas Vander Wal is an information architect best known for coining the term "folksonomy". He is also known for initiating the term "infocloud". His work has primarily dealt with the Web and with information design and structure especially in the context of social technology.

<span class="mw-page-title-main">Gesture recognition</span> Topic in computer science and language technology

Gesture recognition is an area of research and development in computer science and language technology concerned with the recognition and interpretation of human gestures. A subdiscipline of computer vision, it employs mathematical algorithms to interpret gestures.

<span class="mw-page-title-main">Tag (metadata)</span> Keyword assigned to information

In information systems, a tag is a keyword or term assigned to a piece of information. This kind of metadata helps describe an item and allows it to be found again by browsing or searching. Tags are generally chosen informally and personally by the item's creator or by its viewer, depending on the system, although they may also be chosen from a controlled vocabulary.

<span class="mw-page-title-main">Tag cloud</span> Visual representation of word frequency

A tag cloud is a visual representation of text data which is often used to depict keyword metadata on websites, or to visualize free form text. Tags are usually single words, and the importance of each tag is shown with font size or color. When used as website navigation aids, the terms are hyperlinked to items associated with the tag.

Exploratory search is a specialization of information exploration which represents the activities carried out by searchers who are:

Social information processing is "an activity through which collective human actions organize knowledge." It is the creation and processing of information by a group of people. As an academic field Social Information Processing studies the information processing power of networked social systems.

<span class="mw-page-title-main">GroupLens Research</span> Computer science research lab

GroupLens Research is a human–computer interaction research lab in the Department of Computer Science and Engineering at the University of Minnesota, Twin Cities specializing in recommender systems and online communities. GroupLens also works with mobile and ubiquitous technologies, digital libraries, and local geographic information systems.

Collaborative search engines (CSE) are Web search engines and enterprise searches within company intranets that let users combine their efforts in information retrieval (IR) activities, share information resources collaboratively using knowledge tags, and allow experts to guide less experienced people through their searches. Collaboration partners do so by providing query terms, collective tagging, adding comments or opinions, rating search results, and links clicked of former (successful) IR activities to users having the same or a related information need.

Folksonomy is a classification system in which end users apply public tags to online items, typically to make those items easier for themselves or others to find later. Over time, this can give rise to a classification system based on those tags and how often they are applied or searched for, in contrast to a taxonomic classification designed by the owners of the content and specified when it is published. This practice is also known as collaborative tagging, social classification, social indexing, and social tagging. Folksonomy was originally "the result of personal free tagging of information [...] for one's own retrieval", but online sharing and interaction expanded it into collaborative forms. Social tagging is the application of tags in an open online environment where the tags of other users are available to others. Collaborative tagging is tagging performed by a group of users. This type of folksonomy is commonly used in cooperative and collaborative projects such as research, content repositories, and social bookmarking.

Collaborative tagging, also known as social tagging or folksonomy, allows users to apply public tags to online items, typically to make those items easier for themselves or others to find later. It has been argued that these tagging systems can provide navigational cues or "way-finders" for other users to explore information. The notion is that given that social tags are labels users create to represent topics extracted from online documents, the interpretation of these tags should allow other users to predict the contents of different documents efficiently. Social tags are arguably more important in exploratory search, in which the users may engage in iterative cycles of goal refinement and exploration of new information, and interpretation of information contents by others will provide useful cues for people to discover topics that are relevant.

In web analytics, a session, or visit is a unit of measurement of a user's actions taken within a period of time or with regard to completion of a task. Sessions are also used in operational analytics and provision of user-specific recommendations. There are two primary methods used to define a session: time-oriented approaches based on continuity in user activity and navigation-based approaches based on continuity in a chain of requested pages.

A distributed file system for cloud is a file system that allows many clients to have access to data and supports operations on that data. Each data file may be partitioned into several parts called chunks. Each chunk may be stored on different remote machines, facilitating the parallel execution of applications. Typically, data is stored in files in a hierarchical tree, where the nodes represent directories. There are several ways to share files in a distributed architecture: each solution must be suitable for a certain type of application, depending on how complex the application is. Meanwhile, the security of the system must be ensured. Confidentiality, availability and integrity are the main keys for a secure system.

Social media mining is the process of obtaining data from user-generated content on social media in order to extract actionable patterns, form conclusions about users, and act upon the information. Mining supports targeting advertising to users or academic research. The term is an analogy to the process of mining for minerals. Mining companies sift through raw ore to find the valuable minerals; likewise, social media mining sifts through social media data in order to discern patterns and trends about matters such as social media usage, online behaviour, content sharing, connections between individuals, buying behaviour. These patterns and trends are of interest to companies, governments and not-for-profit organizations, as such organizations can use the analyses for tasks such as design strategies, introduce programs, products, processes or services.

<span class="mw-page-title-main">Shumin Zhai</span> Human–computer interaction research scientist

Shumin Zhai is a Chinese-born American Canadian Human–computer interaction (HCI) research scientist and inventor. He is known for his research specifically on input devices and interaction methods, swipe-gesture-based touchscreen keyboards, eye-tracking interfaces, and models of human performance in human-computer interaction. His studies have contributed to both foundational models and understandings of HCI and practical user interface designs and flagship products. He previously worked at IBM where he invented the ShapeWriter text entry method for smartphones, which is a predecessor to the modern Swype keyboard. Dr. Zhai's publications have won the ACM UIST Lasting Impact Award and the IEEE Computer Society Best Paper Award, among others, and he is most known for his research specifically on input devices and interaction methods, swipe-gesture-based touchscreen keyboards, eye-tracking interfaces, and models of human performance in human-computer interaction. Dr. Zhai is currently a Principal Scientist at Google where he leads and directs research, design, and development of human-device input methods and haptics systems.

References

  1. ↑ Dourish, P. and Chalmers, M. (1004). Running out of space: models of information navigation. Proceedings of HCI'94, Glasgow, August 1994.
  2. 1 2 Chen, Chaomei; Cribbin, Timothy; Kuljis, Jasna; MacRedie, Robert (2002). "Footprints of information foragers: Behaviour semantics of visual exploration". International Journal of Human-Computer Studies. 57 (2): 139–163. doi:10.1016/S1071-5819(02)91015-3.
  3. ↑ Dieberger, A.; Dourish, P.; Höök, K.; Resnick, P.; Wexelblat, A. (2000). "Social navigation: Techniques for building more usable systems". Interactions. 7 (6): 36–45. doi:10.1145/352580.352587.
  4. ↑ Svensson, Martin; Höök, Kristina; Cöster, Rickard (2005). "Designing and evaluating kalas: A social navigation system for food recipes". ACM Transactions on Computer-Human Interaction. 12 (3): 374–400. doi:10.1145/1096737.1096739.
  5. 1 2 3 4 5 Pirolli, Peter; Card, Stuart (1999). "Information foraging". Psychological Review. 106 (4): 643–675. doi:10.1037/0033-295X.106.4.643.
  6. 1 2 Bonabeau, E.; Dorigo, M.; Theraulaz, G. (2000). "Inspiration for optimization from social insect behaviour". Nature. 406 (6791): 39–42. doi:10.1038/35017500. PMID   10894532.
  7. 1 2 3 Wexelblat, Alan; Maes, Pattie (1999). "Footprints: History-rich tools for information foraging". Proceedings of the SIGCHI conference on Human factors in computing systems the CHI is the limit - CHI '99. pp. 270–277. doi:10.1145/302979.303060. ISBN   0-201-48559-1.
  8. 1 2 3 4 5 Pirolli, Peter; Card, Stuart (1995). "Information foraging in information access environments". Proceedings of the SIGCHI conference on Human factors in computing systems - CHI '95. pp. 51–58. doi:10.1145/223904.223911. ISBN   0-201-84705-1.
  9. 1 2 Yen, Benjamin P.-C. (2007). "The design and evaluation of accessibility on web navigation". Decision Support Systems. 42 (4): 2219–2235. doi:10.1016/j.dss.2006.07.002.
  10. 1 2 3 Spence, Robert (1999). "A framework for navigation". International Journal of Human-Computer Studies. 51 (5): 919–945. doi:10.1006/ijhc.1999.0265.
  11. ↑ Campbell, Christopher S.; Maglio, Paul P. (1999). "Facilitating navigation in information spaces: Road-signs on the World Wide Web". International Journal of Human-Computer Studies. 50 (4): 309–327. doi:10.1006/ijhc.1998.0246.
  12. 1 2 3 Dieberger, Andreas (1997). "Supporting social navigation on the World Wide Web". International Journal of Human-Computer Studies. 46 (6): 805–825. doi:10.1006/ijhc.1996.0111. hdl: 1853/3525 .
  13. 1 2 Beydoun, Ghassan; Kultchitsky, Roman; Manasseh, Grace (2007). "Evolving semantic web with social navigation". Expert Systems with Applications. 32 (2): 265–276. doi:10.1016/j.eswa.2005.11.035.
  14. ↑ Social Navigation of Information Space. Computer Supported Cooperative Work. 1999. doi:10.1007/978-1-4471-0837-5. ISBN   978-1-85233-090-3.
  15. ↑ Björneborn, Lennart (2011). "Behavioural traces and indirect user-to-user mediation in the participatory library" (PDF). Proceedings of ISSOME 2011, the International Conference on Information Science and Social Media. pp. 151–166.
  16. ↑ Freyne, Jill; Farzan, Rosta; Brusilovsky, Peter; Smyth, Barry; Coyle, Maurice (2007). "Collecting community wisdom". Proceedings of the 12th international conference on Intelligent user interfaces (PDF). pp. 52–61. doi:10.1145/1216295.1216312. ISBN   978-1-59593-481-9. S2CID   6145344.
  17. ↑ United States of AmericaGranted US6064980 A,Jennifer A. Jacobi, Eric A. Benson&Eric A. Benson,"System and methods for collaborative recommendations",published 2000-05-16,issued 2000-05-16, assigned to Amazon.Com, Inc.
  18. ↑ Marlow, Jennifer; Dabbish, Laura; Herbsleb, Jim (2013). "Impression formation in online peer production". Proceedings of the 2013 conference on Computer supported cooperative work. pp. 117–128. doi:10.1145/2441776.2441792. ISBN   978-1-4503-1331-5. S2CID   11432865.
  19. ↑ Angeletou, S; Rowe, M; Alani, H (2011). "Modelling and Analysis of User Behaviour in Online Communities". The Semantic Web – ISWC 2011. Lecture Notes in Computer Science. Vol. 7031. pp. 35–50. doi:10.1007/978-3-642-25073-6_3. ISBN   978-3-642-25072-9.
  20. ↑ Hogg, Tad; Szabo, Gabor (2009). "Dynamics and diversity of online community activities". EPL. 86 (3): 58–65. arXiv: 0803.3482 . Bibcode:2009EL.....8638003H. doi:10.1209/0295-5075/86/38003. S2CID   818245.
  21. ↑ Tumasjan, A; Sprenger, T.O.; Sandner, P.G.; Welpe, I.M. (2010). "Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment". Proceedings of the International AAAI Conference on Web and Social Media. 4: 178–185. doi: 10.1609/icwsm.v4i1.14009 . S2CID   17539846.
  22. ↑ Benevenuto, Fabrício; Rodrigues, Tiago; Cha, Meeyoung; Almeida, Virgílio (2009). "Characterizing user behavior in online social networks". Proceedings of the 9th ACM SIGCOMM conference on Internet measurement. pp. 52–61. CiteSeerX   10.1.1.161.812 . doi:10.1145/1644893.1644900. ISBN   978-1-60558-771-4. S2CID   3022786.
  23. ↑ Furnas, G.W.; Landauer, T.K. (1987). "The vocabulary problem in human-system communication". Communications of the ACM. 30 (11): 964–971. CiteSeerX   10.1.1.118.4768 . doi:10.1145/32206.32212. S2CID   3002280.
  24. ↑ Rivadeneira, A. W.; Gruen, Daniel M.; Muller, Michael J.; Millen, David R. (2007). "Getting our head in the clouds: Toward evaluation studies of tagclouds". Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. pp. 995–998. doi:10.1145/1240624.1240775. ISBN   978-1-59593-593-9.
  25. ↑ Bateman, Scott; Gutwin, Carl; Nacenta, Miguel (2008). "Seeing things in the clouds". Proceedings of the nineteenth ACM conference on Hypertext and hypermedia. pp. 193–202. doi:10.1145/1379092.1379130. ISBN   9781595939852. S2CID   16289413.
  26. ↑ Kuo, Byron Y-L; Hentrich, Thomas; Good, Benjamin M.; Wilkinson, Mark D. (2007). "Tag clouds for summarizing web search results". Proceedings of the 16th international conference on World Wide Web. pp. 1203–1204. doi:10.1145/1242572.1242766. ISBN   9781595936547. S2CID   530240.
  27. ↑ Lohmann, Steffen; Ziegler, Jürgen; Tetzlaff, Lena (2009). "Comparison of Tag Cloud Layouts: Task-Related Performance and Visual Exploration". Human-Computer Interaction – INTERACT 2009. Lecture Notes in Computer Science. Vol. 5726. pp. 392–404. doi:10.1007/978-3-642-03655-2_43. ISBN   978-3-642-03654-5.
  28. ↑ Skoutas, Dimitrios; Alrifai, Mohammad (2011). "Tag clouds revisited". Proceedings of the 20th ACM international conference on Information and knowledge management. pp. 221–230. doi:10.1145/2063576.2063613. ISBN   978-1-4503-0717-8.
  29. 1 2 Venetis, Petros; Koutrika, Georgia; Garcia-Molina, Hector (2011). "On the selection of tags for tag clouds". Proceedings of the fourth ACM international conference on Web search and data mining. pp. 835–844. doi:10.1145/1935826.1935855. ISBN   978-1-4503-0493-1.
  30. ↑ Helic, Denis; Strohmaier, Markus; Trattner, Christoph; Muhr, Markus; Lerman, Kristina (2011). "Pragmatic evaluation of folksonomies". Proceedings of the 20th international conference on World wide web. pp. 417–426. doi:10.1145/1963405.1963465. ISBN   978-1-4503-0632-4.
  31. ↑ Brusilovsky, Peter; Chavan, Girish; Farzan, Rosta (2004). "Social adaptive navigation support for open corpus electronic textbooks". Adaptive Hypermedia and Adaptive Web-Based Systems. Springer-Verlag Berlin Heidelberg. pp. 805–825. ISBN   978-3-540-27780-4.
  32. ↑ Mertens, Robert; Farzan, Rosta; Brusilovsky, Peter (2006). "Social navigation in web lectures". Proceedings of the seventeenth conference on Hypertext and hypermedia. pp. 41–44. doi:10.1145/1149941.1149950. ISBN   978-1-59593-417-8. S2CID   6043216.
  33. ↑ Farzan, Rosta; Brusilovsky, Peter (2008). "AnnotatEd: A social navigation and annotation service for web-based educational resources". New Review of Hypermedia and Multimedia. 14 (1): 3–32. CiteSeerX   10.1.1.153.7859 . doi:10.1080/13614560802357172. ISSN   1361-4568. S2CID   1133351.
  34. ↑ Foth, Marcus (2008). Handbook of Research on Urban Informatics: The Practice and Promise of the Real-Time City: The Practice and Promise of the Real-Time City. IGI Global. pp. 188–189. ISBN   9781605661537.
  35. ↑ Bilandzic, Mark; Foth, Marcus; De Luca, Alexander (2008). "CityFlocks". Proceedings of the 7th ACM conference on Designing interactive systems (PDF). pp. 174–183. doi:10.1145/1394445.1394464. ISBN   978-1-60558-002-9. S2CID   12808486.
  36. ↑ Dieberger, Andreas (1997). "Supporting social navigation on the World Wide Web". International Journal of Human-Computer Studies. 46 (6): 805–825. CiteSeerX   10.1.1.25.8360 . doi:10.1006/ijhc.1996.0111. ISSN   1071-5819.
  37. ↑ Wexelblat, Alan, and Pattie Maes. "Footprints: history-rich tools for information foraging". Proceedings of the SIGCHI conference on Human Factors in Computing Systems. ACM, 1999.
  38. ↑ Brusilovsky, Peter, et al. "Social navigation for educational digital libraries". Procedia Computer Science 1.2 (2010): 2889-2897.
  39. ↑ Karimi, Hassan A., et al. "SoNavNet: a framework for social navigation networks". Proceedings of the 2009 International Workshop on Location Based Social Networks. ACM, 2009.
  40. ↑ Svensson, Martin, et al. "Social navigation of food recipes". Proceedings of the SIGCHI conference on Human factors in computing systems. ACM, 2001.
  41. ↑ Willett, Wesley; Heer, Jeffrey; Agrawala, Maneesh (2007). "Scented Widgets: Improving Navigation Cues with Embedded Visualizations". IEEE Transactions on Visualization and Computer Graphics. 13 (6): 1129–1136. doi:10.1109/TVCG.2007.70589. PMID   17968056.
  42. ↑ Digioia, Paul, and P. Dourish. "Social Navigation as a Model for Usable Security." Symposium on Usable Privacy and Security, SOUPS 2005, Pittsburgh, Pennsylvania, Usa, July 2005:101-108.
  43. ↑ Lichtenthäler, Christina; Peters, Annika; Griffiths, Sascha; Kirsch, Alexandra (2013). "Social Navigation - Identifying Robot Navigation Patterns in a Path Crossing Scenario". Social Robotics. Lecture Notes in Computer Science. Vol. 8239. pp. 84–93. doi:10.1007/978-3-319-02675-6_9. ISBN   978-3-319-02674-9.
  44. ↑ Sinai M B, Partush N, Yadid S, et al. Exploiting Social Navigation[J]. Eprint Arxiv, 2014.
  45. ↑ Vosinakis, Spyros; Papadakis, Ioannis (2011). "Virtual Worlds as Information Spaces: Supporting Semantic and Social Navigation in a shared 3D Environment". 2011 Third International Conference on Games and Virtual Worlds for Serious Applications. pp. 220–227. doi:10.1109/VS-GAMES.2011.41. ISBN   978-1-4577-0316-4.
  46. ↑ Van Den Bosch, Alje; Van Arem, Bart; Mahmod, Mohamed; Misener, James (2011). "Reducing time delays on congested road networks using social navigation". 2011 IEEE Forum on Integrated and Sustainable Transportation Systems. pp. 26–31. doi:10.1109/FISTS.2011.5973596. ISBN   978-1-4577-0990-6.
This page is based on this Wikipedia article
Text is available under the CC BY-SA 4.0 license; additional terms may apply.
Images, videos and audio are available under their respective licenses.