Why People Search for Images using Web Search Engines

What are the intents or goals behind human interactions with image search engines? Knowing why people search for images is of major concern to Web image search engines because user satisfaction may vary as intent varies. Previous analyses of image search behavior have mostly been query-based, focusing on what images people search for, rather than intent-based, that is, why people search for images. To date, there is no thorough investigation of how different image search intents affect users' search behavior. In this paper, we address the following questions: (1)Why do people search for images in text-based Web image search systems? (2)How does image search behavior change with user intent? (3)Can we predict user intent effectively from interactions during the early stages of a search session? To this end, we conduct both a lab-based user study and a commercial search log analysis. We show that user intents in image search can be grouped into three classes: Explore/Learn, Entertain, and Locate/Acquire. Our lab-based user study reveals different user behavior patterns under these three intents, such as first click time, query reformulation, dwell time and mouse movement on the result page. Based on user interaction features during the early stages of an image search session, that is, before mouse scroll, we develop an intent classifier that is able to achieve promising results for classifying intents into our three intent classes. Given that all features can be obtained online and unobtrusively, the predicted intents can provide guidance for choosing ranking methods immediately after scrolling

Revisiting revisitation in computer interaction: organic bookmark management.

According to Milic-Frayling et al. (2004), there are two general ways of user browsing i.e. search (finding a website where the user has never visited before) and revisitation (returning to a website where the user has visited in the past). The issue of search is relevant to search engine technology, whilst revisitation concerns web usage and browser history mechanisms. The support for revisitation is normally through a set of functional built-in icons e.g. History, Back, Forward and Bookmarks. Nevertheless, for returning web users, they normally find it is easier and faster to re-launch an online search again, rather than spending time to find a particular web site from their personal bookmark and history records. Tauscher and Greenberg (1997) showed that revisiting web pages forms up to 58% of the recurrence rate of web browsing. Cockburn and McKenzie (2001) also stated that 81% of web pages have been previously visited by the user. According to Obendorf et al. (2007), revisitation can be divided into four classifications based on time: short-term (72.6% revisits within an hour), medium-term (12% revisits within a day and 7.8% revisits within a week), and long-term (7.6% revisits longer than a week)

Why We Read Wikipedia

Wikipedia is one of the most popular sites on the Web, with millions of users relying on it to satisfy a broad range of information needs every day. Although it is crucial to understand what exactly these needs are in order to be able to meet them, little is currently known about why users visit Wikipedia. The goal of this paper is to fill this gap by combining a survey of Wikipedia readers with a log-based analysis of user activity. Based on an initial series of user surveys, we build a taxonomy of Wikipedia use cases along several dimensions, capturing users' motivations to visit Wikipedia, the depth of knowledge they are seeking, and their knowledge of the topic of interest prior to visiting Wikipedia. Then, we quantify the prevalence of these use cases via a large-scale user survey conducted on live Wikipedia with almost 30,000 responses. Our analyses highlight the variety of factors driving users to Wikipedia, such as current events, media coverage of a topic, personal curiosity, work or school assignments, or boredom. Finally, we match survey responses to the respondents' digital traces in Wikipedia's server logs, enabling the discovery of behavioral patterns associated with specific use cases. For instance, we observe long and fast-paced page sequences across topics for users who are bored or exploring randomly, whereas those using Wikipedia for work or school spend more time on individual articles focused on topics such as science. Our findings advance our understanding of reader motivations and behavior on Wikipedia and can have implications for developers aiming to improve Wikipedia's user experience, editors striving to cater to their readers' needs, third-party services (such as search engines) providing access to Wikipedia content, and researchers aiming to build tools such as recommendation engines.Comment: Published in WWW'17; v2 fixes caption of Table