Search engine researchers typically depict search as the solitary activity of an individual searcher. In contrast, results from our critical-incident survey of 150 users on Amazon's Mechanical Turk service suggest that social interactions play an important role throughout the search process. Our main contribution is that we have integrated models from previous work in sensemaking and information seeking behavior to present a canonical social model of user activities before, during, and after search, suggesting where in the search process even implicitly shared information may be valuable to individual searchers.
Surprisingly, researchers have thought about navigating and browsing for information as a single user activity, centered on eliciting users' information needs and improving the relevance of search results. For example, Choo, Detlor & Turnbull @cite_1 discussed categories of search behaviors and motivations in information seeking, but they overlooked the role of other individuals in search. On the other hand, library scientists @cite_3 have observed for some time that friends and colleagues may be valuable information resources during search. Similarly, recent authors have begun to recognize the prevalence and benefits of @cite_9 @cite_0 .
@cite_0 @cite_9 @cite_1 @cite_3
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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. This paper presents findings from a study of how knowledge workers use the Web to seek external information as part of their daily work. Thirty-four users from seven companies took part in the study. Participants were mainly IT specialists, managers, and research marketing consulting staff working in organizations that included a large utility company, a major bank, and a consulting firm. Participants answered a detailed questionnaire and were interviewed individually in order to understand their information needs and information seeking preferences. A custom-developed WebTracker software application was installed on each of their work place PCs, and participants' Web-use activities were then recorded continuously during two-week periods. The WebTracker recorded how participants used the browser to seek information on the Web: it logged menu choices, button bar selections, and keystroke actions, allowing browsing and searching sequences to be reconstructed. In a second round of personal interviews, participants recalled critical incidents of using information from the Web. Data from the two interviews and the WebTracker logs constituted the database for analysis. Sixty-one significant episodes of information seeking were identified. A model was developed to describe the common repertoires of information seeking that were observed. On one axis of the model, episodes were plotted according to the four scanning modes identified by Aguilar (1967), Weick and Daft (1983): undirected viewing, conditioned viewing, informal search, and formal search. Each mode is characterized by its own information needs and information seeking strategies. On the other axis of the model, episodes were plotted according to the occurrence of one or more of the six categories of information seeking behaviors identified by Ellis (1989, 1990): starting, chaining, browsing, differentiating, monitoring, and extracting. The study suggests that a behavioral framework that relates motivations (Aguilar) and moves (Ellis) may be helpful in analyzing patterns of Web-based information seeking. This paper presents a modified diary study that investigated how people performed personally motivated searches in their email, in their files, and on the Web. Although earlier studies of directed search focused on keyword search, most of the search behavior we observed did not involve keyword search. Instead of jumping directly to their information target using keywords, our participants navigated to their target with small, local steps using their contextual knowledge as a guide, even when they knew exactly what they were looking for in advance. This stepping behavior was especially common for participants with unstructured information organization. The observed advantages of searching by taking small steps include that it allowed users to specify less of their information need and provided a context in which to understand their results. We discuss the implications of such advantages for the design of personal information management tools. The lack of user activity on search results was until recently perceived as a sign of user dissatisfaction from retrieval performance, often, referring to such inactivity as a failed search (negative search abandonment). However, recent studies suggest that some search tasks can be achieved in the contents of the results displayed without the need to click through them (positive search abandonment); thus they emphasize the need to discriminate between successful and failed searches without follow-up clicks. In this paper, we study users’ inactivity on search results in relation to their pursued search goals and investigate the impact of displayed results on user clicking decisions. Our study examines two types of post-query user inactivity: pre-determined and post-determined depending on whether the user started searching with a preset intention to look for answers only within the result snippets and did not intend to click through the results, or the user inactivity was decided after the user had reviewed the list of retrieved documents. Our findings indicate that 27 of web searches in our sample are conducted with a pre-determined intention to look for answers in the results’ list and 75 of them can be satisfied in the contents of the displayed results. Moreover, in nearly half the queries that did not yield result visits, the desired information is found in the result snippets. This article proposes a process to retrieve the URL of a document for which metadata records exist in a digital library catalog but a pointer to the full text of the document is not available. The process uses results from queries submitted to Web search engines for finding the URL of the corresponding full text or any related material. We present a comprehensive study of this process in different situations by investigating different query strategies applied to three general purpose search engines (Google, Yahoo!, MSN) and two specialized ones (Scholar and CiteSeer), considering five user scenarios. Specifically, we have conducted experiments with metadata records taken from the Brazilian Digital Library of Computing (BDBComp) and The DBLP Computer Science Bibliography (DBLP). We found that Scholar was the most effective search engine for this task in all considered scenarios and that simple strategies for combining and re-ranking results from Scholar and Google significantly improve the retrieval quality. Moreover, we study the influence of the number of query results on the effectiveness of finding missing information as well as the coverage of the proposed scenarios. 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. We describe results from Web search log studies aimed at elucidating user behaviors associated with queries and destination URLs that appear with different frequencies. We note the diversity of information goals that searchers have and the differing ways that goals are specified. We examine rare and common information goals that are specified using rare or common queries. We identify several significant differences in user behavior depending on the rarity of the query and the destination URL. We find that searchers are more likely to be successful when the frequencies of the query and destination URL are similar. We also establish that the behavioral differences observed for queries and goals of varying rarity persist even after accounting for potential confounding variables, including query length, search engine ranking, session duration, and task difficulty. Finally, using an information-theoretic measure of search difficulty, we show that the benefits obtained by search and navigation actions depend on the frequency of the information goal. An important component of routine visual behavior is the ability to find one item in a visual world filled with other, distracting items. This ability to performvisual search has been the subject of a large body of research in the past 15 years. This paper reviews the visual search literature and presents a model of human search behavior. Built upon the work of Neisser, Treisman, Julesz, and others, the model distinguishes between a preattentive, massively parallel stage that processes information about basic visual features (color, motion, various depth cues, etc.) across large portions of the visual field and a subsequent limited-capacity stage that performs other, more complex operations (e.g., face recognition, reading, object identification) over a limited portion of the visual field. The spatial deployment of the limited-capacity process is under attentional control. The heart of the guided search model is the idea that attentional deployment of limited resources isguided by the output of the earlier parallel processes. Guided Search 2.0 (GS2) is a revision of the model in which virtually all aspects of the model have been made more explicit and or revised in light of new data. The paper is organized into four parts: Part 1 presents the model and the details of its computer simulation. Part 2 reviews the visual search literature on preattentive processing of basic features and shows how the GS2 simulation reproduces those results. Part 3 reviews the literature on the attentional deployment of limited-capacity processes in conjunction and serial searches and shows how the simulation handles those conditions. Finally, Part 4 deals with shortcomings of the model and unresolved issues. 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. The advent of the Internet has made the transmission of personally identifiable information more common and often unintended by the user. As personal information becomes more accessible, individuals worry that businesses misuse the information that is collected while they are online. Organizations have tried to mitigate this concern in two ways: (1) by offering privacy policies regarding the handling and use of personal information and (2) by offering benefits such as financial gains or convenience. In this paper, we interpret these actions in the context of the information-processing theory of motivation. Information-processing theories, also known as expectancy theories in the context of motivated behavior, are built on the premise that people process information about behavior-outcome relationships. By doing so, they are forming expectations and making decisions about what behavior to choose. Using an experimental setting, we empirically validate predictions that the means to mitigate privacy concerns are associated with positive valences resulting in an increase in motivational score. In a conjoint analysis exercise, 268 participants from the United States and Singapore face trade-off situations, where an organization may only offer incomplete privacy protection or some benefits. While privacy protections (against secondary use, improper access, and error) are associated with positive valences, we also find that financial gains and convenience can significantly increase individuals' motivational score of registering with a Web site. We find that benefits-monetary reward and future convenience-significantly affect individuals' preferences over Web sites with differing privacy policies. We also quantify the value of Web site privacy protection. Among U.S. subjects, protection against errors, improper access, and secondary use of personal information is worth @math 44.62. Finally, our approach also allows us to identify three distinct segments of Internet users-privacy guardians, information sellers, and convenience seekers. Search engine researchers typically depict search as the solitary activity of an individual searcher. In contrast, results from our critical-incident survey of 150 users on Amazon's Mechanical Turk service suggest that social interactions play an important role throughout the search process. Our main contribution is that we have integrated models from previous work in sensemaking and information seeking behavior to present a canonical social model of user activities before, during, and after search, suggesting where in the search process both explicitly and implicitly shared information may be valuable to individual searchers. The information needs of search engine users vary in complexity, depending on the task they are trying to accomplish. Some simple needs can be satisfied with a single query, whereas others require a series of queries issued over a longer period of time. While search engines effectively satisfy many simple needs, searchers receive little support when their information needs span session boundaries. In this work, we propose methods for modeling and analyzing user search behavior that extends over multiple search sessions. We focus on two problems: (i) given a user query, identify all of the related queries from previous sessions that the same user has issued, and (ii) given a multi-query task for a user, predict whether the user will return to this task in the future. We model both problems within a classification framework that uses features of individual queries and long-term user search behavior at different granularity. Experimental evaluation of the proposed models for both tasks indicates that it is possible to effectively model and analyze cross-session search behavior. Our findings have implications for improving search for complex information needs and designing search engine features to support cross-session search tasks. We are experiencing an unprecedented increase of content contributed by users in forums such as blogs, social networking sites and microblogging services. Such abundance of content complements content on web sites and traditional media forums such as news papers, news and financial streams, and so on. Given such plethora of information there is a pressing need to cross reference information across textual services. For example, commonly we read a news item and we wonder if there are any blogs reporting related content or vice versa. In this paper, we present techniques to automate the process of cross referencing online information content. We introduce methodologies to extract phrases from a given "query document" to be used as queries to search interfaces with the goal to retrieve content related to the query document. In particular, we consider two techniques to extract and score key phrases. We also consider techniques to complement extracted phrases with information present in external sources such as Wikipedia and introduce an algorithm called RelevanceRank for this purpose. We discuss both these techniques in detail and provide an experimental study utilizing a large number of human judges from Amazons's Mechanical Turk service. Detailed experiments demonstrate the effectiveness and efficiency of the proposed techniques for the task of automating retrieval of documents related to a query document. This paper reports on a novel technique for literature indexing and searching in a mechanized library system. The notion of relevance is taken as the key concept in the theory of information retrieval and a comparative concept of relevance is explicated in terms of the theory of probability. The resulting technique called “Probabilistic Indexing,” allows a computing machine, given a request for information, to make a statistical inference and derive a number (called the “relevance number”) for each document, which is a measure of the probability that the document will satisfy the given request. The result of a search is an ordered list of those documents which satisfy the request ranked according to their probable relevance. The paper goes on to show that whereas in a conventional library system the cross-referencing (“see” and “see also”) is based solely on the “semantical closeness” between index terms, statistical measures of closeness between index terms can be defined and computed. Thus, given an arbitrary request consisting of one (or many) index term(s), a machine can elaborate on it to increase the probability of selecting relevant documents that would not otherwise have been selected. Finally, the paper suggests an interpretation of the whole library problem as one where the request is considered as a clue on the basis of which the library system makes a concatenated statistical inference in order to provide as an output an ordered list of those documents which most probably satisfy the information needs of the user. In this paper, we introduce "task trail" as a new concept to understand user search behaviors. We define task to be an atomic user information need. Web search logs have been studied mainly at session or query level where users may submit several queries within one task and handle several tasks within one session. Although previous studies have addressed the problem of task identification, little is known about the advantage of using task over session and query for search applications. In this paper, we conduct extensive analyses and comparisons to evaluate the effectiveness of task trails in three search applications: determining user satisfaction, predicting user search interests, and query suggestion. Experiments are conducted on large scale datasets from a commercial search engine. Experimental results show that: (1) Sessions and queries are not as precise as tasks in determining user satisfaction. (2) Task trails provide higher web page utilities to users than other sources. (3) Tasks represent atomic user information needs, and therefore can preserve topic similarity between query pairs. (4) Task-based query suggestion can provide complementary results to other models. The findings in this paper verify the need to extract task trails from web search logs and suggest potential applications in search and recommendation systems. Understanding how people interact when searching is central to the study of Interactive Information Retrieval (IIR). Most of the prior work has either been conceptual, observational or empirical. While this has led to numerous insights and findings regarding the interaction between users and systems, the theory has lagged behind. In this paper, we extend the recently proposed search economic theory to make the model more realistic. We then derive eight interaction based hypotheses regarding search behaviour. To validate the model, we explore whether the search behaviour of thirty-six participants from a lab based study is consistent with the theory. Our analysis shows that observed search behaviours are in line with predicted search behaviours and that it is possible to provide credible explanations for such behaviours. This work describes a concise and compact representation of search behaviour providing a strong theoretical basis for future IIR research. While many digital libraries focus on supporting defined tasks that require targeted searching, there is potential for enabling serendipitous discovery that can serve multiple purposes from aiding with the targeted search to suggesting new approaches, methods and ideas. In this research we embedded a tool in a novel interface to suggest other pages to examine in order to assess how that tool might be used while doing focused searching. While only 40 of the participants used the tool, all assessed its usefulness or perceived usefulness. Most participants used it as a source of new terms and concepts to support their current tasks; a few noted the novelty and perceived its potential value in serving as a stimulant. User feedback is considered to be a critical element in the information seeking process. An important aspect of the feedback cycle is relevance assessment that has progressively become a popular practice in web searching activities and interactive information retrieval (IR). The value of relevance assessment lies in the disambiguation of the user's information need, which is achieved by applying various feedback techniques. Such techniques vary from explicit to implicit and help determine the relevance of the retrieved documents. The former type of feedback is usually obtained through the explicit and intended indication of documents as relevant (positive feedback) or irrelevant (negative feedback). Explicit feedback is a robust method for improving a system's overall retrieval performance and producing better query reformulations [1], at the expense of users' cognitive resources. On the other hand, implicit feedback techniques tend to collect information on search behavior in a more intelligent and unobtrusive manner. By doing so, they disengage the users from the cognitive burden of document rating and relevance judgments. Information-seeking activities such as reading time, saving, printing, selecting and referencing have been all treated as indicators of relevance, despite the lack of sufficient evidence to support their effectiveness [2]. Besides their apparent differences, both categories of feedback techniques determine document relevance with respect to the cognitive and situational levels of the interactive dialogue that occurs between the user and the retrieval system [5]. However, this approach does not account for the dynamic interplay and adaptation that takes place between the different dialogue levels, but most importantly it does not consider the affective dimension of interaction. Users interact with intentions, motivations and feelings apart from real-life problems and information objects, which are all critical aspects of cognition and decision-making [3][4]. By evaluating users' affective response towards an information object (e.g. a document), prior and post to their exposure to it, a more accurate understanding of the object's properties and degree of relevance to the current information need may be facilitated. Furthermore, systems that can detect and respond accordingly to user emotions could potentially improve the naturalness of human-computer interaction and progressively optimize their retrieval strategy. The current study investigates the role of emotions in the information seeking process, as the latter are communicated through multi-modal interaction, and reconsiders relevance feedback with respect to what occurs on the affective level of interaction as well. Addressing user's information needs has been one of the main goals of Web search engines since their early days. In some cases, users cannot see their needs immediately answered by search results, simply because these needs are too complex and involve multiple aspects that are not covered by a single Web or search results page. This typically happens when users investigate a certain topic in domains such as education, travel or health, which often require collecting facts and information from many pages. We refer to this type of activities as "research missions". These research missions account for 10 of users' sessions and more than 25 of all query volume, as verified by a manual analysis that was conducted by Yahoo! editors. We demonstrate in this paper that such missions can be automatically identified on-the-fly, as the user interacts with the search engine, through careful runtime analysis of query flows and query sessions. The on-the-fly automatic identification of research missions has been implemented in Search Pad, a novel Yahoo! application that was launched in 2009, and that we present in this paper. Search Pad helps users keeping trace of results they have consulted. Its novelty however is that unlike previous notes taking products, it is automatically triggered only when the system decides, with a fair level of confidence, that the user is undertaking a research mission and thus is in the right context for gathering notes. Beyond the Search Pad specific application, we believe that changing the level of granularity of query modeling, from an isolated query to a list of queries pertaining to the same research missions, so as to better reflect a certain type of information needs, can be beneficial in a number of other Web search applications. Session-awareness is growing and it is likely to play, in the near future, a fundamental role in many on-line tasks: this paper presents a first step on this path. Information retrieval systems (e.g., web search engines) are critical for overcoming information overload. A major deficiency of existing retrieval systems is that they generally lack user modeling and are not adaptive to individual users, resulting in inherently non-optimal retrieval performance. For example, a tourist and a programmer may use the same word "java" to search for different information, but the current search systems would return the same results. In this paper, we study how to infer a user's interest from the user's search context and use the inferred implicit user model for personalized search. We present a decision theoretic framework and develop techniques for implicit user modeling in information retrieval. We develop an intelligent client-side web search agent (UCAIR) that can perform eager implicit feedback, e.g., query expansion based on previous queries and immediate result reranking based on clickthrough information. Experiments on web search show that our search agent can improve search accuracy over the popular Google search engine.