Despite the services of sophisticated search engines like Google, there are a number of interesting information sources which are useful but largely inaccessible to current Web users. These information sources are often ad-hoc, location-specific and only useful for users over short periods of time, or relate to tacit knowledge of users or implicit knowledge in crowds. The solution presented in this paper addresses these problems by introducing an integrated concept of "location" and "presence" across the physical and virtual worlds enabling ad-hoc socializing of users interested in, or looking for similar information. While the definition of presence in the physical world is straightforward - through a spatial location and vicinity at a certain point in time - their definitions in the virtual world are neither obvious nor trivial. Based on a detailed analysis we provide an integrated spatial model spanning both worlds which enables us to define presence of users in a unified way. This integrated model allows us to enable ad-hoc socializing of users browsing the Web with users in the physical world specific to their joint information needs and allows us to unlock the untapped information sources mentioned above. We describe a proof-of-concept implementation of our model and provide an empirical analysis based on real-world experiments.
Our work examines Web revisitation patterns. Everybody revisits Web pages, but their reasons for doing so can differ depending on the particular Web page, their topic of interest, and their intent. To characterize how people revisit Web content, we analyzed five weeks of Web interaction logs of over 612,000 users. We supplemented these findings by a survey intended to identify the intent behind the observed revisitation. Our analysis reveals four primary revisitation patterns, each with unique behavioral, content, and structural characteristics. Through our analysis we illustrate how understanding revisitation patterns can enable Web sites to provide improved navigation, Web browsers to predict users' destinations, and search engines to better support fast, fresh, and effective finding and re-finding. We present a study which investigated how and why users of Mozilla Firefox use multiple tabs and windows during web browsing. The detailed web browsing usage of 21 participants was logged over a period of 13 to 21 days each, and was supplemented by qualitative data from diary entries and interviews. Through an examination of several measures of their tab usage, we show that our participants had a strong preference for the use of tabs rather than multiple windows. We report the reasons they cited for using tabs, and the advantages over multiple windows. We identify several common tab usage patterns which browsers could explicitly support. Finally, we look at how tab usage affects web page revisitation. Most of our participants switched tabs more often than they used the back button, making tab switching the second most important navigation mechanism in the browser, after link clicking. As the Web has become integrated into daily life, understanding how individuals spend their time online impacts domains ranging from public policy to marketing. It is difficult, however, to measure even simple aspects of browsing behavior via conventional methods---including surveys and site-level analytics---due to limitations of scale and scope. In part addressing these limitations, large-scale Web panel data are a relatively novel means for investigating patterns of Internet usage. In one of the largest studies of browsing behavior to date, we pair Web histories for 250,000 anonymized individuals with user-level demographics---including age, sex, race, education, and income---to investigate three topics. First, we examine how behavior changes as individuals spend more time online, showing that the heaviest users devote nearly twice as much of their time to social media relative to typical individuals. Second, we revisit the digital divide, finding that the frequency with which individuals turn to the Web for research, news, and healthcare is strongly related to educational background, but not as closely tied to gender and ethnicity. Finally, we demonstrate that browsing histories are a strong signal for inferring user attributes, including ethnicity and household income, a result that may be leveraged to improve ad targeting. Query logs, the patterns of activity left by millions of users, contain a wealth of information that can be mined to aid personalization. We perform a large-scale study of Yahoo! search engine logs, tracking 1.35 million browser-cookies over a period of 6 months. We define metrics to address questions such as 1) How much history is available?, 2) How do users' topical interests vary, as reflected by their queries?, and 3) What can we learn from user clicks? We find that there is significantly more expected history for the user of a randomly picked query than for a randomly picked user. We show that users exhibit consistent topical interests that vary between users. We also see that user clicks indicate a variety of special interests. Our findings shed light on user activity and can inform future personalization efforts. We show that incorporating user behavior data can significantly improve ordering of top results in real web search setting. We examine alternatives for incorporating feedback into the ranking process and explore the contributions of user feedback compared to other common web search features. We report results of a large scale evaluation over 3,000 queries and 12 million user interactions with a popular web search engine. We show that incorporating implicit feedback can augment other features, improving the accuracy of a competitive web search ranking algorithms by as much as 31 relative to the original performance. User Navigation Behavior Mining (UNBM) mainly studies the problems of extracting the interesting user access patterns from user access sequences (UAS), which are usually used for user access prediction and web page recommendation. Through analyzing the real world web data, we find most of user access sequences carrying hybrid features of different patterns, rather than a single one. Browsing the web has been shown to be a highly recurrent activity. Aimed to optimize the browsing experience, extensive previous research has been carried out on users' revisitation behavior. However, the conventional definition for revisitation, which only considers page loading activities by monitoring http requests initiated by the browser, largely underestimates users' intended revisitation activities with tabbed browsers. Thus, we introduce a goal-oriented definition and a refined revisitation measurement based on page viewings in tabbed browsers. An empirical analysis of statistics taken from a client-side log study showed that although the overall revisitation rate remained relatively constant, tabbed browsing has introduced new behaviors warrant future investigations. We examine the properties of all HTTP requests generated by a thousand undergraduates over a span of two months. Preserving user identity in the data set allows us to discover novel properties of Web traffic that directly affect models of hypertext navigation. We find that the popularity of Web sites--the number of users who contribute to their traffic--lacks any intrinsic mean and may be unbounded. Further, many aspects of the browsing behavior of individual users can be approximated by log-normal distributions even though their aggregate behavior is scale-free. Finally, we show that users' click streams cannot be cleanly segmented into sessions using timeouts, affecting any attempt to model hypertext navigation using statistics of individual sessions. We propose a strictly logical definition of sessions based on browsing activity as revealed by referrer URLs; a user may have several active sessions in their click stream at any one time. We demonstrate that applying a timeout to these logical sessions affects their statistics to a lesser extent than a purely timeout-based mechanism.