Measuring inter-site engagement

Many large online providers offer a variety of content sites (e.g. news, sport, e-commerce). These providers endeavor to keep users accessing and interacting with their sites, that is to engage users by spending time using their sites and to return regularly to them. They do so by serving users the most relevant content in an attractive and enticing manner. Due to their highly varied content, each site is usually studied and optimized separately. However, these online providers aim not only to engage users with individual sites, but across all sites in their network. In these cases, site engagement should be examined not only within individual sites, but also across the entire content provider network. This paper investigates intersite engagement, that is, site engagement within a network of sites, by defining a global measure of engagement that captures the effect sites have on the engagement on other sites. As an application, we look at the effect of web page layout and structure, which we refer to as web page stylistics, on intersite engagement on Yahoo! properties. Through the analysis of 50 popular Yahoo! sites and a sample of 265,000 users and 19.4M online sessions, we demonstrate that the stylistic components of a web page on a site can be used to predict inter-site engagement across the Yahoo! network of sites. Intersite engagement is a new big data problem as overall it implies analyzing dozen of sites visited by hundreds of millions of people generating billions of sessions.

[1]  Nicholas J. Belkin,et al.  Display time as implicit feedback: understanding task effects , 2004, SIGIR '04.

[2]  Rosie Jones,et al.  Beyond the session timeout: automatic hierarchical segmentation of search topics in query logs , 2008, CIKM '08.

[3]  Filippo Menczer,et al.  Agents, bookmarks and clicks: a topical model of web navigation , 2010, HT '10.

[4]  Anna Chmiel,et al.  Scaling of human behavior during portal browsing. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

[5]  Susan T. Dumais,et al.  Improving Web Search Ranking by Incorporating User Behavior Information , 2019, SIGIR Forum.

[6]  Bernardo A. Huberman,et al.  The Pulse of News in Social Media: Forecasting Popularity , 2012, ICWSM.

[7]  Thomas Beauvisage The dynamics of personal territories on the web , 2009, LINK.

[8]  Thomas Beauvisage The dynamics of personal territories on the web , 2009, Hypertext.

[9]  Bernard J. Jansen,et al.  Evaluating the effectiveness of and patterns of interactions with automated searching assistance , 2005, J. Assoc. Inf. Sci. Technol..

[10]  Gabriella Kazai,et al.  Towards a science of user engagement. , 2011 .

[11]  Ryen W. White,et al.  Mining the search trails of surfing crowds: identifying relevant websites from user activity , 2008, WWW.

[12]  Gabriella Kazai,et al.  Towards a science of user engagement (Position Paper) , 2011 .

[13]  Ryen W. White,et al.  Large-scale analysis of individual and task differences in search result page examination strategies , 2012, WSDM '12.

[14]  Andy Cockburn,et al.  What do web users do? An empirical analysis of web use , 2001, Int. J. Hum. Comput. Stud..

[15]  Yunfei Chen,et al.  Evaluating the visual quality of web pages using a computational aesthetic approach , 2011, WSDM '11.

[16]  Ricardo Baeza-Yates,et al.  The effect of links on networked user engagement , 2012, WWW.

[17]  Eric Brill,et al.  Improving web search ranking by incorporating user behavior information , 2006, SIGIR.

[18]  Ryen W. White,et al.  Understanding web browsing behaviors through Weibull analysis of dwell time , 2010, SIGIR.

[19]  M. V. Simkin,et al.  A theory of web traffic , 2007, 0711.1235.