The importance of tasks in information retrieval (IR) has been long argued for, addressed in different ways, often ignored, and frequently revisited. For decades, scholars made a case for the role that a user’s task plays in how and why that user engages in search and what a search system should do to assist. But for the most part, the IR community has been too focused on query processing and assuming a search task to be a collection of user queries, often ignoring if or how such an assumption addresses the users accomplishing their tasks. With emerging areas of conversational agents and proactive IR, understanding and addressing users’ tasks has become more important than ever before. In this paper, we provide various perspectives on where the state-of-the-art is with regard to tasks in IR, what are some of the bottlenecks in deriving and using task information, and how do we go forward from here. In addition to covering relevant literature, the paper provides a synthesis of historical and current perspectives on understanding, extracting, and addressing task-focused search. To ground ongoing and future research in this area, we present a new framing device for tasks using a tree-like structure and various moves on that structure that allow different interpretations and applications. Presented as a combination of synthesis of ideas and past works, proposals for future research, and our perspectives on technical, social, and ethical considerations, this paper is meant to help revitalize the interest and future work in task-based IR.

The Third WEPIR 2020 workshop builds on the success of the first two WEPIR meetings held at CHIIR 2018 and CHIIR 2019. WEPIR 2020 again brings together researchers from different backgrounds interested in continuing to explore and advance the evaluation of personalisation in information retrieval. Similar to the first two workshops, WEPIR 2020 has a strong emphasis on active participation by workshop attendees. This was very successfully achieved in the first two workshops by the use of workshop breakout groups exploring topics related to personalisation and information retrieval, and the evaluation of personalisation in information retrieval in general, with subsequent report back to the workshop as a whole. However, a key difference for WEPIR 2020 is that while the first two workshops focused on developing and articulating principles and ideas relating general topics relating to these topics, identified as interesting and important by the attendees at the workshops, WEPIR 2020 focuses breakout discussion on a number of relevant specific use cases of the evaluation of personalisation in information retrieval. A use case is assigned to each breakout group with the plan being to have more than one group work on each use case. The task for each group is to identify specific relevant factors relating to the use case in terms of user activities, data to be collected, ethical issues, and evaluation metrics. Groups will make reports of their discussions to the assembled workshop in the final session with discussion of the alternative solutions relating to the same use case, and contrasting the issues raised by the different use cases. The overall goal of the workshop is to work towards developing a general set of principles and guidelines for addressing the evaluation of specific instances of the use of personalisation in information retrieval tasks. This is consistent with the activities and goals of the first two workshops, but represents a significant progression of the activities towards concrete outcomes of benefit to those exploring personalisation in search, the issues arising in its evaluation, and how researchers might go about tackling this in specific situations.

Personalization of information retrieval (PIR) is aimed at tailoring a search toward individual users and user groups by taking account of additional information about users besides their queries. In the past two decades or so, PIR has received extensive attention in both academia and industry. This article surveys the literature of personalization in text retrieval, following a framework for aspects or factors that can be used for personalization. The framework consists of additional information about users that can be explicitly obtained by asking users for their preferences, or implicitly inferred from users' search behaviors. Users' characteristics and contextual factors such as tasks, time, location, etc., can be helpful for personalization. This article also addresses various issues including when to personalize, the evaluation of PIR, privacy, usability, etc. Based on the extensive review, challenges are discussed and directions for future effort are suggested.

In Interactive Information Retrieval (IIR) experiments the user's gaze motion on web pages is often recorded with eye tracking. The data is used to analyze gaze behavior or to identify Areas of Interest (AOI) the user has looked at. So far, tools for analyzing eye tracking data have certain limitations in supporting the analysis of gaze behavior in IIR experiments. Experiments often consist of a huge number of different visited web pages. In existing analysis tools the data can only be analyzed in videos or images and AOIs for every single web page have to be specified by hand, in a very time consuming process. In this work, we propose the reading protocol software which breaks eye tracking data down to the textual level by considering the HTML structure of the web pages. This has a lot of advantages for the analyst. First and foremost, it can easily be identified on a large scale what has actually been viewed and read on the stimuli pages by the subjects. Second, the web page structure can be used to filter to AOIs. Third, gaze data of multiple users can be presented on the same page, and fourth, fixation times on text can be exported and further processed in other tools. We present the software, its validation, and example use cases with data from three existing IIR experiments.

The second WEPIR 2019 workshop brings together researchers with different backgrounds interested in continuing to explore and advance the evaluation of personalisation in information retrieval. The workshop builds on the first WEPIR workshop held at CHIIR 2018, and will focus on further developing a common understanding of the challenges, requirements and practical limitations of meaningful evaluation of personalisation in information retrieval. In particular, the planned outcome of the workshop is to progress the work from WEPIR 2018 towards the development of concrete proposals for novel and innovative methodologies to support evaluation of personalised information retrieval from both the perspectives of the user experience in interactive search settings, and of user models for personalised information retrieval and their algorithmic incorporation in the search process.

According to the cognitive viewpoint of information retrieval (IR) research, a search task can be conceptualized as a sequence of information seeking intentions which both motivate and are influenced by search behaviors. While the behavioral effects of task features have been thoroughly discussed in a large body of literature, how different information seeking intentions in query segments serve as bridges between task and Web search behavior still remains unexplored. To develop a more comprehensive, multi-level (i.e., task level, intention level, and behavior level) understanding of Web search, the authors analyzed intention and search behavior data collected from 693 query segments generated by 40 participants in a controlled lab setting, seeking to answer two main research questions: 1) from task to intention : how do different task features affect users' information seeking intentions at different stages of a search session? 2) from intention to behavior : How is a user's search behavior associated with their information seeking intentions in the current and next query segments respectively? The results demonstrate that: 1) Task features significantly affected the frequency of occurrence of most of the information seeking intentions, and these effects gradually faded away as search sessions proceeded; 2) The presences of a variety of intentions in both current and subsequent query segments were connected with and detectable by different subsets of behavioral measures. This study contributes to the understanding of the connections between task, intentions in query segments, and search behavior, and thereby has implications for designing system affordances for supporting different intentions and search activities in various task stages and contexts.

When users are looking for information on the Web, they show different behavior for different task types, e.g., for fact finding vs. information gathering tasks. For example, related work in this area has investigated how this behavior can be measured and applied to distinguish between easy and difficult tasks. In this work, we look at the searcher's behavior in the domain of journalism for four different task types, and additionally, for two different topics in each task type. Search behavior is measured with a number of session variables and correlated to subjective measures such as task difficulty, task success and the usefulness of documents. We acknowledge prior results in this area that task difficulty is correlated to user effort and that easy and difficult tasks are distinguishable by session variables. However, in this work, we emphasize the role of the task topic - in and of itself - over parameters such as the search results and read content pages, dwell times, session variables and subjective measures such as task difficulty or task success. With this knowledge researchers should give more attention to the task topic as an important influence factor for user behavior.

The purpose of the WEPIR 2018 workshop is to bring together researchers from different backgrounds, interested in advancing the evaluation of personalisation in information retrieval. The workshop focus is on the development of a common understanding of the challenges, requirements and practical limitations of meaningful evaluation of personalisation in information retrieval. The planned outcome of the workshop is the proposal of methodologies to support evaluation of personalised information retrieval from both the perspectives of the user experience in interactive search settings, and of user models for personalised information retrieval and their algorithmic incorporation in the search process.

There is broad consensus in the field of IR that search is complex in many use cases and applications, both on the Web and in domain specific collections, and both professionally and in our daily life. Yet our understanding of complex search tasks, in comparison to simple look up tasks, is fragmented at best. The workshop addresses many open research questions: What are the obvious use cases and applications of complex search? What are essential features of work tasks and search tasks to take into account? And how do these evolve over time--With a multitude of information, varying from introductory to specialized, and from authoritative to speculative or opinionated, when to show what sources of information? How does the information seeking process evolve and what are relevant differences between different stages? With complex task and search process management, blending searching, browsing, and recommendations, and supporting exploratory search to sensemaking and analytics, UI and UX design pose an overconstrained challenge. How do we evaluate and compare approaches? Which measures should be taken into account? Supporting complex search tasks requires new collaborations across the fields of CHI and IR, and the proposed workshop will bring together a diverse group of researchers to work together on one of the greatest challenges of our field.

It has been shown that people attempt to accomplish a variety of intentions during the course of an information seeking session, and there is reason to believe that these different information seeking intentions can benefit from system support tailored to each such intention. We address the problem of predicting the presence of such intentions during an information seeking session, through analysis of observable user search behaviors. We present results of a study of 40 participants, each working on two different journalism tasks, which investigated how their search behaviors could indicate their intentions. Using 725 query-segments captured from this study, we demonstrate that information seeking intentions can be predicted with a simple classification model using a linear combination of search behavior features that can be logged with a browser plug-in.

We present a method for extracting the self-reported intentions of users engaged in an information seeking episode. We recruited participants to conduct search sessions and subsequently asked them to self-report their intentions. A total of 27 users participated in a lab study, during which they worked on two search tasks. After each search session, participants indicated their intentions during that session while viewing a video replay. Results indicate that the set of search intentions provided to participants was sufficient to account for intentions in four journalism-related information seeking tasks: a copy editing task, interview preparation task, relationships task, and story pitch task. The results also suggest regular patterns in intentions that can be exploited for identification of task type as well as potential applications to personalization and recommendation during a search episode.

As a significant contextual factor in information search, topic knowledge has been gaining increased research attention. We report on a study of the relationship between information searchers' topic knowledge and their search behaviors, and on an attempt to predict searchers' topic knowledge from their behaviors during the search. Data were collected in a controlled laboratory experiment with 32 undergraduate journalism student participants, each searching on 4 tasks of different types. In general, behavioral variables were not found to have significant differences between users with high and low levels of topic knowledge, except the mean first dwell time on search result pages. Several models were built to predict topic knowledge using behavioral variables calculated at 3 different stages of search episodes: the first‐query‐round, the middle point of the search, and the end point. It was found that a model using some search behaviors observed in the first query round led to satisfactory prediction results. The results suggest that early‐session search behaviors can be used to predict users' topic knowledge levels, allowing personalization of search for users with different levels of topic knowledge, especially in order to assist users with low topic knowledge.

Colleagues, friends, let me begin by expressing how pleased, and humbly honored I am to be a recipient of the Gerard Salton Award. Gerry was a great man, and to receive the award named for him is very special. For me personally, it is especially meaningful, given the sometime disputatious nature of our professional interactions, and what seemed, on the surface, to be quite different ideas about information retrieval. I say, on the surface, because in the end, I believe that he and I both shared the same goal for the field, although we approached it from quite different positions. In this presentation, I will speak at some length on that goal, and on how I think it might be best addressed. I am humbled also by the honor of having joined the ranks of the previous recipients of this award; the founders, leaders and innovators in information retrieval (IR), from the earliest beginnings of the field to today. It has been my distinct good fortune to have known all of the previous recipients, to have collaborated with many of them, to have argued with all of them, to have learned from them, and, I hope, to have been able to appropriately incorporate their insights into my own work. Today, following the example of many of my predecessors, I'd like to take this opportunity to, in Sue Dumais's words of 2009, "present a personal reflection on information retrieval." This will include an overview of my history in IR, a discussion of my personal take on its proper goals, and on how those might be best achieved, and saying something about the challenges that IR theory, experiment and practice face both now, and in the future, and how we might best address those challenges. I came to the field of IR from a starting point in information science; specifically, with the concern of addressing general problems of information in society. I initially thought that the best way to do this would be to establish a firm framework for a science of information. Acting on my then understanding of what a science constituted, I began the project of defining information. Fortunately for me, I did this at University College, London, with B.C. Brookes as my Ph.D. supervisor, and Steve Robertson my office mate. It didn't take long for me to be disabused of a) the idea that information could be defined; and b) that defining its phenomena of interest was a necessary precondition to a "real" science. Instead, perhaps under the influence of the great pragmatist, Jeremy Bentham, founder of University College, I turned to attempting to develop a concept of information which would lead to being able to predict its effect on a person's state of knowledge, which I took at that time as what IR systems should be attempting to do.

Personalization of information retrieval tailors search towards individual users to meet their particular information needs by taking into account information about users and their contexts, often through implicit sources of evidence such as user behaviors. This study looks at users' dwelling behavior on documents and several contextual factors: the stage of users' work tasks, task type, and users' knowledge of task topics, to explore whether or not taking account contextual factors could help infer document usefulness from dwell time. A controlled laboratory experiment was conducted with 24 participants, each coming 3 times to work on 3 subtasks in a general work task. The results show that task stage could help interpret certain types of dwell time as reliable indicators of document usefulness in certain task types, as was topic knowledge, and the latter played a more significant role when both were available. This study contributes to a better understanding of how dwell time can be used as implicit evidence of document usefulness, as well as how contextual factors can help interpret dwell time as an indicator of usefulness. These findings have both theoretical and practical implications for using behaviors and contextual factors in the development of personalization systems.

User domain knowledge affects search behaviors and search success. Predicting a user's knowledge level from implicit evidence such as search behaviors could allow an adaptive information retrieval system to better personalize its interaction with users. This study examines whether user domain knowledge can be predicted from search behaviors by applying a regression modeling analysis method. We identify behavioral features that contribute most to a successful prediction model. A user experiment was conducted with 40 participants searching on task topics in the domain of genomics. Participant domain knowledge level was assessed based on the users' familiarity with and expertise in the search topics and their knowledge of MeSH (Medical Subject Headings) terms in the categories that corresponded to the search topics. Users' search behaviors were captured by logging software, which includes querying behaviors, document selection behaviors, and general task interaction behaviors. Multiple regression analysis was run on the behavioral data using different variable selection methods. Four successful predictive models were identified, each involving a slightly different set of behavioral variables. The models were compared for the best on model fit, significance of the model, and contributions of individual predictors in each model. Each model was validated using the split sampling method. The final model highlights three behavioral variables as domain knowledge level predictors: the number of documents saved, the average query length, and the average ranking position of the documents opened. The results are discussed, study limitations are addressed, and future research directions are suggested.

Personalization of support for information seeking depends crucially on the information retrieval system's knowledge of the task that led the person to engage in information seeking. Users work during information search sessions to satisfy their task goals, and their activity is not random. To what degree are there patterns in the user activity during information search sessions? Do activity patterns reflect the user's situation as the user moves through the search task under the influence of his or her task goal? Do these patterns reflect aspects of different types of information-seeking tasks? Could such activity patterns identify contexts within which information seeking takes place? To investigate these questions, we model sequences of user behaviors in two independent user studies of information search sessions (N = 32 users, 128 sessions, and N = 40 users, 160 sessions). Two representations of user activity patterns are used. One is based on the sequences of page use; the other is based on a cognitive representation of information acquisition derived from eye movement patterns in service of the reading process. One of the user studies considered journalism work tasks; the other concerned background research in genomics using search tasks taken from the TREC Genomics Track. The search tasks differed in basic dimensions of complexity, specificity, and the type of information product (intellectual or factual) needed to achieve the overall task goal. The results show that similar patterns of user activity are observed at both the cognitive and page use levels. The activity patterns at both representation layers are able to distinguish between task types in similar ways and, to some degree, between tasks of different levels of difficulty. We explore relationships between the results and task difficulty and discuss the use of activity patterns to explore events within a search session. User activity patterns can be at least partially observed in server-side search logs. A focus on patterns of user activity sequences may contribute to the development of information systems that better personalize the user's search experience.

Knowing, in real time, whether a current searcher in an information retrieval system finds the search task difficult can be valuable for tailoring the system's support for that searcher. This study investigated searcher's behaviors at different stages of the search process; they are: 1) first-round point at the beginning of the search, right before searchers issued their second query; 2) middle point, when searchers proceeded to the middle of the search process, and 3) end point, when searchers finished the whole task. We compared how the behavioral features calculated at these three points were different between difficult and easy search tasks, and identified behavioral features during search sessions that can be used in real-time to predict perceived task difficulty. In addition, we compared the prediction performance at different stages of search process. Our results show that a number of user behavioral measures at all three points differed between easy and difficult tasks. Query interval time, dwell time on viewed documents, and number of viewed documents per query were important predictors of task difficulty. The results also indicate that it is possible to make relatively accurate prediction of task difficulty at the first query round of a search. Our findings can help search systems predict task difficulty which is necessary in personalizing support for the individual searcher.

Can the activity patterns of page use during information search sessions discriminate between different types of information seeking tasks? We model sequences of interactions with search result and content pages during information search sessions. Two representations are created: the sequences of page use and a cognitive representation of page interactions. The cognitive representation is based on logged eye movement patterns of textual information acquisition via the reading process. Page sequence actions from task sessions (n=109) in a user study are analyzed. The study tasks differed from one another in basic dimensions of complexity, specificity,level, and the type of information product (intellectual or factual). The results show that differences in task types can be measured at both the level of observations of page type sequences and at the level of cognitive activity on the pages. We discuss the implications for personalization of search systems, measurement of task similarity and the development of user-centered information systems that can support the user's current and expected search intentions.

We report on an investigation of behavioral differences between users in difficult and easy search tasks. Behavioral factors that can be used in real-time to predict task difficulty are identified. User data was collected in a controlled lab experiment (n=38) where each participant completed four search tasks in the genomics domain. We looked at user behaviors that can be obtained by systems at three levels, distinguished by the time point when the measurements can be done. They are: 1) first-round level at the beginning of the search, 2) accumulated level during the search, and 3) whole-session level by the end of the search. Results show that a number of user behaviors at all three levels differed between easy and difficult tasks. Models predicting task difficulty at all three levels were developed and evaluated. A real-time model incorporating first-round and accumulated levels of behaviors (FA) had fairly good prediction performance (accuracy 83%; precision 88%), which is comparable with the model using the whole-session level behaviors which are not real-time (accuracy 75%; precision 92%). We also found that for efficiency purpose, using only a limited number of significant variables (FC_FA) can obtain a prediction accuracy of 75%, with a precision of 88%. Our findings can help search systems predict task difficulty and adapt search results to users.

Personalization of search results offers the potential for significant improvement in information retrieval performance. User interactions with the system and documents during information-seeking sessions provide a wealth of information about user preferences and their task goals. In this paper, we propose methods for analyzing and modeling user search behavior in search sessions to predict document usefulness and then using information to personalize search results. We generate prediction models of document usefulness from behavior data collected in a controlled lab experiment with 32 participants, each completing uncontrolled searching for 4 tasks in the Web. The generated models are then tested with another data set of user search sessions in radically different search tasks and constrains. The documents predicted useful and not useful by the models are used to modify the queries in each search session using a standard relevance feedback technique. The results show that application of the models led to consistently improved performance over a baseline that did not take account of user interaction information. These findings have implications for designing systems for personalized search and improving user search experience.

Click through events in search results pages (SERPs) are not reliable implicit indicators of document relevance. A user's task and domain knowledge are key factors in recognition and link selection and the most useful SERP document links may be those that best match the user's domain knowledge. User study participants rated their knowledge of genomics MeSH terms before conducting 2004 TREC Genomics Track tasks. Each participant's document knowledge was represented by their knowledge of the indexing MeSH terms. Results show high, intermediate, and low domain knowledge groups had similar document selection SERP rank distributions. SERP link selection distribution varied when participant knowledge of the available documents was analyzed. High domain knowledge participants usually selected a document with the highest personal knowledge rating. Low domain knowledge participants were reasonably successful at selecting available documents of which they had the most knowledge, while intermediate knowledge participants often failed to do so. This evidence for knowledge effects on SERP link selection may contribute to understanding the potential for personalization of search results ranking based on user domain knowledge.

We report findings on how the user's perception of task difficulty changes before and after searching for information to solve tasks. We found that while in one type of task, the dependent task, this did not change, in another, the parallel task, it did. The findings have implications on designing systems that can provide assistance to users with their search and task solving strategies.

This study uses regression modeling to predict a user's domain knowledge level (DK) from implicit evidence provided by certain search behaviors. A user study (n=35) with recall-oriented search tasks in the genomic domain was conducted. A number of regression models of a person's DK, were generated using different behavior variable selection methods. The best model highlights three behavior variables as DK predictors: the number of documents saved, the average query length, and the average ranking position of documents opened. The model is validated using the split sampling method. Limitations and future research directions are discussed.

We report on an investigation into people’s behaviors on information search tasks, specifically the relation between eye movement patterns and task characteristics. We conducted two independent user studies (n = 32 and n = 40), one with journalism tasks and the other with genomics tasks. The tasks were constructed to represent information needs of these two different users groups and to vary in several dimensions according to a task classification scheme. For each participant we classified eye gaze data to construct models of their reading patterns. The reading models were analyzed with respect to the effect of task types and Web page types on reading eye movement patterns. We report on relationships between tasks and individual reading behaviors at the task and page level. Specifically we show that transitions between scanning and reading behavior in eye movement patterns and the amount of text processed may be an implicit indicator of the current task type facets. This may be useful in building user and task models that can be useful in personalization of information systems and so address design demands driven by increasingly complex user actions with information systems. One of the contributions of this research is a new methodology to model information search behavior and investigate information acquisition and cognitive processing in interactive information tasks

Dwell time as a user behavior has been found in previous studies to be an unreliable predictor of document usefulness, with contextual factors such as the user's task needing to be considered in its interpretation. Task stage has been shown to influence search behaviors including usefulness judgments, as has task type. This paper reports on an investigation of how task stage and task type may help predict usefulness from the time that users spend on retrieved documents, over the course of several information seeking episodes. A 3-stage controlled experiment was conducted with 24 participants, each coming 3 times to work on 3 sub-tasks of a general task, couched either as "parallel" or "dependent" task type. The full task was to write a report on the general topic, with interim documents produced for each sub-task. Results show that task stage can help in inferring document usefulness from decision time, especially in the parallel task. The findings can be used to increase accuracy in predicting document usefulness and accordingly in personalizing search for multi-session tasks.

In this paper, we report findings on how user behaviors vary in tasks with different difficulty levels as well as of different types. Two behavioral signals: document dwell time and number of content pages viewed per query, were found to be able to help the system detect when users are working with difficult tasks.

Abstract This study explores the relationships between work task and interactive information search behavior. Work task was conceptualized based on a faceted classification of task. An experiment was conducted with six work‐task types and simulated work‐task situations assigned to 24 participants. The results indicate that users present different behavior patterns to approach useful information for different work tasks: They select information systems to search based on the work tasks at hand, different work tasks motivate different types of search tasks, and different facets controlled in the study play different roles in shaping users' interactive information search behavior. The results provide empirical evidence to support the view that work tasks and search tasks play different roles in a user's interaction with information systems and that work task should be considered as a multifaceted variable. The findings provide a possibility to make predictions of a user's information search behavior from his or her work task, and vice versa. Thus, this study sheds light on task‐based information seeking and search, and has implications in adaptive information retrieval (IR) and personalization of IR.

Abstract We report on a study that investigated the efficacy of four different interactive information retrieval (IIR) systems, each designed to support a specific information‐seeking strategy (ISS). These systems were constructed using different combinations of IR techniques (i.e., combinations of different methods of representation, comparison, presentation and navigation), each of which was hypothesized to be well suited to support a specific ISS. We compared the performance of searchers in each such system, designated “experimental,” to an appropriate “baseline” system, which implemented the standard specified query and results list model of current state‐of‐the‐art experimental and operational IR systems. Four within‐subjects experiments were conducted for the purpose of this comparison. Results showed that each of the experimental systems was superior to its baseline system in supporting user performance for the specific ISS (that is, the information problem leading to that ISS) for which the system was designed. These results indicate that an IIR system, which intends to support more than one kind of ISS, should be designed within a framework which allows the use and combination of different IR support techniques for different ISSs.

Abstract Many studies have demonstrated that people engage in a variety of different information behaviors when engaging in information seeking. However, standard information retrieval systems such as Web search engines continue to be designed to support mainly one such behavior, specified searching. This situation has led to suggestions that people would be better served by information retrieval systems which support different kinds of information‐seeking strategies. This article reports on an experiment comparing the retrieval effectiveness of an integrated interactive information retrieval (IIR) system which adapts to support different information‐seeking strategies with that of a standard baseline IIR system. The experiment, with 32 participants each searching on eight different topics, indicates that using the integrated IIR system resulted in significantly better user satisfaction with search results, significantly more effective interaction, and significantly better usability than that using the baseline system.

This paper reports on an experiment comparing the retrieval effectiveness of an interactive information retrieval (IIR) system which adapts to support different information seeking strategies, with that of a standard baseline IIR system. The experiment, with 32 subjects each searching on 8 different topics, indicates that using the integrated IIR system resulted in significantly better performance, including user satisfaction with search results, significantly more effective interaction, and significantly better usability than using the baseline system.

Abstract Most information systems share a common assumption: information seeking is discrete. Such an assumption neither reflects real‐life information seeking processes nor conforms to the perspective of phenomenology, “life is a journey constituted by continuous acquisition of knowledge.” Thus, this study develops and validates a theoretical model that explains successive search experience for essentially the same information problem. The proposed model is called Multiple Information Seeking Episodes (MISE), which consists of four dimensions: problematic situation, information problem, information seeking process, episodes. Eight modes of multiple information seeking episodes are identified and specified with properties of the four dimensions of MISE. The results partially validate MISE by finding that the original MISE model is highly accurate, but less sufficient in characterizing successive searches; all factors in the MISE model are empirically confirmed, but new factors are identified as well. The revised MISE model is shifted from the user‐centered to the interaction‐centered perspective, taking into account factors of searcher, system, search activity, search context, information attainment, and information use activities.

Recent research has had some success using the length of time a user displays a document in their web browser as implicit feedback for document preference. However, most studies have been confined to specific search domains, such as news, and have not considered the effects of task on display time, and the potential impact of this relationship on the effectiveness of display time as implicit feedback. We describe the results of an intensive naturalistic study of the online information-seeking behaviors of seven subjects during a fourteen-week period. Throughout the study, subjects' online information-seeking activities were monitored with various pieces of logging and evaluation software. Subjects were asked to identify the tasks with which they were working, classify the documents that they viewed according to these tasks, and evaluate the usefulness of the documents. Results of a user-centered analysis demonstrate no general, direct relationship between display time and usefulness, and that display times differ significantly according to specific task, and according to specific user.

This paper reports on and discusses a set of user experiments using the TREC 2003 Web interactive track protocol. The focus is on comparing humans and machine algorithms in terms of performance in a topic distillation task. We also investigated the effect of the search results layout in supporting the users' effort.We have demonstrated that machines can perform nearly as well as people on the topic distillation task. Given a system tailored to the task there is significant performance improvement and finally, given a presentation that supports the task, there is strong user satisfaction.

Query length in best-match information retrieval (IR) systems is well known to be positively related to effectiveness in the IR task, when measured in experimental, non-interactive environments. However, in operational, interactive IR systems, query length is quite typically very short, on the order of two to three words. We report on a study which tested the effectiveness of a particular query elicitation technique in increasing initial searcher query length, and which tested the effectiveness of queries elicited using this technique, and the relationship in general between query length and search effectiveness in interactive IR. Results show that the specific technique results in longer queries than a standard query elicitation technique, that this technique is indeed usable, that the technique results in increased user satisfaction with the search, and that query length is positively correlated with user satisfaction with the search.

We describe results from an ongoing project that considers question types and document features and their relationship to retrieval techniques. We examine eight document features from the top 25 documents retrieved from 74 questions and find that lists and FAQs occur in more documents judged relevant to task-oriented questions than those judged relevant to fact-oriented questions.

No abstract available.

This review examines a sample of interactive information retrieval (IR) systems within the framework of a design challenge set forth by John Bennett in 1971. Bennett directed his challenge to a group of information scientists who attended The User Interface for Interactive Search of Bibliographic Data Bases Workshop. During the workshop, participants exchanged their insights and experiences on various aspects of user interface requirements for interactive IR systems and discussed system design features that could result in the design of more effective user interfaces for information retrieval. The main goals of this review are to characterize interactive IR design trends over time, examine the extent to which IR systems have responded to Bennett's design challenge, and to suggest potential future research directions and challenges.

This study investigates the use and effectiveness of an ,advanced information retrieval (IR) system (INQUERY).64 novice IR system users were studied in their use of a baseline version of INQUERY compared with one of three experimental versions, each offering a different level of interaction with a relevance feedback facility for automatic query reformulation.Results, in an inforumtion filtering task, indicate that: these subjects, after minimal training, were able to use the baseline system reasonably effectively; availability and use of relevance feedback increased retrieval effectiveness; and increased opportunity for user interaction with and control of relevance feedback made the interactions more efficient and usable while maintaining or increasing effectivehess.

We report on the design and evaluation of a visualization tool for Information Retrieval (IR) systems that aims to help the end user in the following respects: .As art indicator of document relevance, the tool graphically provides specific query related information about irtdividual documents .As a diagnosis tool, it graphically provides aggregate information about the query results that could help in identifying how the different query terms influence the retrieval and ranking of documents.Two different experiments using TREC-4 data were conducted to evaluate the effectiveness of tlus tool.Results, while mixed, indicate that visualization of this sort may provide useful support for judging the relevance of documents, in particular by enabling users to make more accurate decisions about which documents to inspect in detail.Problems in evaluation of such tools in interactive environments are dtscussed 1 Permission to make digitat/hard copy of all part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage, the copyright notice, the title of the publication and its date appear, and notice.is given that copying is by

Five independently generated Boolean query formulations for ten different TREC topics were produced by ten different expert online searchers. These different formulations were grouped, and the groups, and combinations of them, were used as searches against the TREC test collection, using the INQUERY probabilistic inference network retrieval engine. Results show that progressive combination of query formulations leads to progressively improving retrieval performance. Results were compared against the performance of INQUERY natural language based queries, and in combination with them. The issue of recall as a performance measure in large databases was raised, since overlap between the searches conducted in this study, and the TREC-1 searches, was smaller than expected.

No abstract available.

In this paper, we propose a method for specifying the functionality of an intelligent interface to large-scale information retrieval systems, and for implementing those functions in an operational environment. The method is based on a progressive, three-stage model of intelligent information support; a high-level cognitive task analysis of the information retrieval problem; a low-level specification of the host system functionality; and, derivation of explicit relations between the system functions and the cognitive tasks. This method is applied, by example, in the context of the European Space Agency Information Retrieval Service, with some specific suggestions for implementation of a stage one intelligent interface to that system.

The introduction of automated information retrieval (IR) systems was met with great enthusiasm and predictions that manual literature searching soon would be replaced. Three decades later, IR systems have not progressed to the stage where any but the dedicated few can operate them without a highly skilled human intermediary acting as interface between user and system. In the interim, we have learned that the retrieval process is extremely complex both in terms of understanding people and their communication and in terms of understanding scientific information and technical vocabulary. Experiments with new techniques suggest to many the possibility of eliminating the human intermediary, either in large part or altogether; others would argue that the retrieval problems are too complex to be resolved for more than highly restricted domains. The possibility of eliminating the human intermediary is of current research interest to the several disciplines that are represented on this panel.

We discuss the complexity of explanation activity in human-human goal-directed dialogue, and suggest that this complexity ought to be taken account of in the design of explanation in human-computer interaction. We propose a general model of clarity in human-computer systems, of which explanation is one component. On the bases of: this model; of a model of human-intermediary interaction in the document retrieval situation as one of cooperative model-building for the purpose of developing an appropriate search formulation; and, on the results of empirical observation of human user-human intermediary interaction in information systems, we propose a model for explanation by the computer intermediary in information retrieval.

Information Science has gained new prominence at Rutgers through the merger of information-related disciplines into the new School of Communication, Information, and Library Studies. The new school brings a multi-disciplinary focus to information phenomena, processes and human information behavior, with wide-ranging impact on instructional programs and research. The School offers a broad-based Ph.D. program, two masters programs, and is planning a new undergraduate program in information studies. Faculty research addresses related areas of information system design, individual human information behavior, information dissemination and communication, and information policy and social impact. © 1988 John Wiley & Sons, Inc.

We report on a project which attempts to classify representations of the anomalous states of knowledge (ASKs) of users of document retrieval systems on the basis of structural characteristics of the representations, and which specifies different retrieval strategies and ranking mechanisms for each ASK class. The classification and retrieval strategy specification is based on 53 real problem statements, 35 of which have a total of 250 evaluated documents. Four facets of the ASK structures have been tentatively identified, whose combinations determine the method and order of application of five basic ranking strategies. This work is still in progress, so results presented here are incomplete.

article Using discourse analysis for the design of information retrieval interaction mechanisms Share on Authors: H. M. Brooks University of London University of LondonView Profile , N. J. Belkin The City University, London The City University, LondonView Profile Authors Info & Claims ACM SIGIR ForumVolume 17Issue 4Summer 1983 pp 31–47https://doi.org/10.1145/1013230.511800Online:01 June 1983Publication History 11citation321DownloadsMetricsTotal Citations11Total Downloads321Last 12 Months16Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

No abstract available.

Abstract This paper aims to deduce the fundamental phenomena of information science, starting from two premises: that information science is a problem‐oriented discipline concerned with the effective transfer of desired information from human generator to human user, and that the single notion common to all concepts of information now extant is that of change of structure . From these premises, a spectrum of information concepts is derived, and a partition of that spectrum particular to the purposes of information science is described. From this partition, the terms text and information (both in information science) are defined, and the fundamental phenomena of information science are deduced: the text and its structure, the structure of the recipient and changes in that structure, and the structure of the sender and the structuring of the text. These phenomena are seen as the basic components of the mechanisms of the channel , which have been the traditional area of interest to information science. Some implications of this approach for research in information science are discussed in this paper. And, finally, the question of the ethics of theoretical research in information science is raised, and a restrictive condition is proposed.

Abstract There is increasing realization of the importance of information concepts in the development of information science. Although some research in this field has been carried out in English‐speaking countries, a much larger body of work has developed in the USSR, most of which is unknown to information scientists in the non‐socialist countries. The present paper discusses some basic aspects of this research, reviews some significant new approaches to determination of information concepts, and appends a selected bibliography to serve as a guide to the Soviet literature of information for information science.