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Strong regularities in World Wide Web sur ng

Strong regularities in World Wide Web sur ng,Science,B. Huberman,P. Pirolli,J. Pitkow,R. Lukose

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Strong regularities in World Wide Web sur ng   (Citations: 184)
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Abstract One of the ,most common ,modes ,of accessing ,information ,in the ,World Wide Web (WWW) is surfing ,from one document ,to another ,along hyperlinks. Several ,large empirical studies have revealed common ,patterns of surfing ,behavior. A model ,which assumes that users make a sequence of decisions to proceed to another page, continuing aslong as the value of the current page exceeds some threshold, yields the probability distribution for the number of pages, or depth, that a user visits within a Web site. This model was verified by comparing,its predictions with detailed measurements ,of surfing patterns. It also explains the observed ,Zipf-like distributions in page ,hits observed ,at WWW sites. Huberman et al,2 The exponential ,growth ,of World ,Wide Web ,(WWW) is making ,it the ,standard
Journal: Science , 1997
Cumulative Annual
    • ...These sequences can be mapped to article clickstreams, each of which records the navigation of a user from one article to another [18,19]...

    Johan Bollen, et al. Clickstream data yields high-resolution maps of science

    • ...By accounting for the varieties of behavior present in user activity, our model provides a better fit to observed data than the previous model in [14]...
    • ...Modeling User Behavior: Another line of work has focused on modeling user behavior [4, 10, 14]...
    • ...We show that while our data conforms to a power law as in [14], the exponent of the distribution best fitting our data is substantially different from that predicted in prior work...
    • ...While our study is based on search-induced behavior, [14] studied trails created from more undirected browsing...
    • ...When creating these post-query trails, we introduce an additional criterion which terminates a trail upon navigation to a site other than that of the clicked query result as in [14]...
    • ...This observation is in accordance with the study by Huberman et al. in [14]...
    • ...While both our study and [14] obtain a power law, we note that the exponent of our power law differs significantly from the exponent of 1.5 that was observed and theoretically derived in [14]...
    • ...While both our study and [14] obtain a power law, we note that the exponent of our power law differs significantly from the exponent of 1.5 that was observed and theoretically derived in [14]...
    • ...Before delving into the details of our proposed model, we note the reasons for which we believe our observed power law differs from the one observed and predicted in [14]...
    • ...First, the nature of the web has changed dramatically since the study in [14]...
    • ...Search-induced trails are likely to be shorter than random-surfing trails for two reasons: (i) typically, searchers seek specific information and when they find what they are looking for, they quickly end their trails, moving on to the next task in hand, while in [14], the assumption is that users continue browsing until the benefit (enjoyment) of the pages encountered becomes less than the “cost” of browsing, and (ii) in the case when ...

    Josh Attenberg, et al. Modeling and predicting user behavior in sponsored search

    • ...Studies found that users demonstrated regularities in their surfing patterns [12]...

    Huijun Xiong, et al. User-Assisted Host-Based Detection of Outbound Malware Traffic

    • ...39 the list (Huberman et al. 1998). While this framework can incorporate any distribution of how users visit sequences of web pages and how they view stories presented in a list on individual pages, we consider a simple model that holds that users view all 15 stories presented on a page and some fraction cf of users who view the current front page proceed to the next front page...

    Kristina Lerman, et al. Stochastic Models of Large-Scale Human Behavior on the Web

    • ...Other examples include the law of Web surfing [8] and the growth dynamics of the World Wide Web [7]...

    Dennis M. Wilkinson. Strong regularities in online peer production

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