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.
There are two principle approaches to investigate users' browsing behavior: such as @cite_16 @cite_17 analyze server access logs. @cite_16 shows that users often feature different behavior patterns, rather than a single one. The results in @cite_17 confirm previous findings about long-tailed distributions in site traffic. @cite_23 @cite_1 analyze search engine interaction logs to gain insights into the query behavior. @cite_23 investigated how users' search behavior can be used as feedback to improve the ranking of query results. @cite_1 found that after a few hundred queries a user's topical interest distribution converges. collect data on the client side using, e.g., browser plugins that log all user actions. @cite_9 focused on demographic factors, i.e., how age, sex, etc. affect users' browsing behavior. In @cite_11 the authors identify different types of revisitation behavior, providing recommendations towards web browser, search engines, and web design. @cite_8 @cite_12 investigated in detail tabbed browsing, i.e., the benefits of multiple tabs within a browser window.
@cite_11 @cite_8 @cite_9 @cite_1 @cite_23 @cite_16 @cite_12 @cite_17
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Abstract Using the clickstream data recorded in Web server log files, the authors develop and estimate a model of the browsing behavior of visitors to a Web site. Two basic aspects of browsing behavior are examined: (1) the visitor’s decisions to continue browsing (by submitting an additional page request) or to exit the site and (2) the length of time spent viewing each page. The authors propose a type II tobit model that captures both aspects of browsing behavior and handles the limitations of server log-file data. The authors fit the model to the individual-level browsing decisions of a random sample of 5000 visitors to the Web site of an Internet automotive company. Empirical results show that visitors’ propensity to continue browsing changes dynamically as a function of the depth of a given site visit and the number of repeat visits to the site. The dynamics are consistent both with “within-site lock-in” or site “stickiness” and with learning that carries over repeat visits. In particular, repeat vis... In this paper, we present the results of a user study on exploratory search activities in a social science digital library. We conducted a user study with 32 participants with a social sciences background—16 postdoctoral researchers and 16 students—who were asked to solve a task on searching related work to a given topic. The exploratory search task was performed in a 10-min time slot. The use of certain search activities is measured and compared to gaze data recorded with an eye tracking device. We use a novel tree graph representation to visualise the users’ search patterns and introduce a way to combine multiple search session trees. The tree graph representation is capable of creating one single tree for multiple users and identifying common search patterns. In addition, the information behaviour of students and postdoctoral researchers is being compared. The results show that search activities on the stratagem level are frequently utilised by both user groups. The most heavily used search activities were keyword search, followed by browsing through references and citations, and author searching. The eye tracking results showed an intense examination of documents metadata, especially on the level of citations and references. When comparing the group of students and postdoctoral researchers, we found significant differences regarding gaze data on the area of the journal name of the seed document. In general, we found a tendency of the postdoctoral researchers to examine the metadata records more intensively with regard to dwell time and the number of fixations. By creating combined session trees and deriving subtrees from those, we were able to identify common patterns like economic (explorative) and exhaustive (navigational) behaviour. Our results show that participants utilised multiple search strategies starting from the seed document, which means that they examined different paths to find related publications. The paper proposes identifying relevant information sources from the history of combined searching and browsing behavior of many Web users. While it has been previously shown that user interactions with search engines can be employed to improve document ranking, browsing behavior that occurs beyond search result pages has been largely overlooked in prior work. The paper demonstrates that users' post-search browsing activity strongly reflects implicit endorsement of visited pages, which allows estimating topical relevance of Web resources by mining large-scale datasets of search trails. We present heuristic and probabilistic algorithms that rely on such datasets for suggesting authoritative websites for search queries. Experimental evaluation shows that exploiting complete post-search browsing trails outperforms alternatives in isolation (e.g., clickthrough logs), and yields accuracy improvements when employed as a feature in learning to rank for Web search. Recent advances in search users' click modeling consider both users' search queries and click skip behavior on documents to infer the user's perceived relevance. Most of these models, including dynamic Bayesian networks (DBN) and user browsing models (UBM), use probabilistic models to understand user click behavior based on individual queries. The user behavior is more complex when her actions to satisfy her information needs form a search session, which may include multiple queries and subsequent click behaviors on various items on search result pages. Previous research is limited to treating each query within a search session in isolation, without paying attention to their dynamic interactions with other queries in a search session. Investigating this problem, we consider the sequence of queries and their clicks in a search session as a task and propose a task-centric click model (TCM). TCM characterizes user behavior related to a task as a collective whole. Specifically, we identify and consider two new biases in TCM as the basis for user modeling. The first indicates that users tend to express their information needs incrementally in a task, and thus perform more clicks as their needs become clearer. The other illustrates that users tend to click fresh documents that are not included in the results of previous queries. Using these biases, TCM is more accurately able to capture user search behavior. Extensive experimental results demonstrate that by considering all the task information collectively, TCM can better interpret user click behavior and achieve significant improvements in terms of ranking metrics of NDCG and perplexity.