Jacek Gwizdka

FotoFile: a consumer multimedia organization and retrieval system

FotoFile is an experimental system for multimediaorganization and retrieval, based upon the design goal of makingmultimedia content accessible to non-expert users. Search andretrieval are done in terms that are natural to the task. Thesystem blends human and automatic annotation methods. It extendstextual search, browsing, and retrieval technologies to supportmultimedia data types.

Email in personal information management

Emails conduit function means the inbox, folders, search, and sort are used to support core PIM functions of task management, personal archiving, and contact management.

What Can Searching Behavior Tell Us About the Difficulty of Information Tasks? A Study of Web Navigation

Task has been recognized as an influential factor in information seeking behavior. An increasing number of studies are concentrating on the specific characteristics of the task as independent variables to explain associated information-seeking activities. This paper examines the relationships between operational measures of information search behavior, subjectively perceived post-task difficulty and objective task complexity in the context of factual information-seeking tasks on the web. A question-driven, web-based information-finding study was conducted in a controlled experimental setting. The study participants performed nine search tasks of varying complexity. Subjective task difficulty was found to be correlated with many measures that characterize the searchers activities. Four of those measures, the number of the unique web pages visited, the time spent on each page, the degree of deviation from the optimal path and the degree of the navigation paths linearity, were found to be good predictors of subjective task difficulty. Objective task complexity was found to affect the relative importance of those predictors and to affect subjective assessment of task difficulty.

Implicit measures of lostness and success in web navigation

In two studies, we investigated the ability of a variety of structural and temporal measures computed from a web navigation path to predict lostness and task success. The users task was to find requested target information on specified websites. The web navigation measures were based on counts of visits to web pages and other statistical properties of the web usage graph (such as compactness, stratum, and similarity to the optimal path). Subjective lostness was best predicted by similarity to the optimal path and time on task. The best overall predictor of success on individual tasks was similarity to the optimal path, but other predictors were sometimes superior depending on the particular web navigation task. These measures can be used to diagnose user navigational problems and to help identify problems in website design.

Search behaviors in different task types

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. Task types have been shown to influence search behaviors including usefulness judgments. This paper reports on an investigation of user behaviors associated with different task types. Twenty-two undergraduate journalism students participated in a controlled lab experiment, each searching on four tasks which varied on four dimensions: complexity, task product, task goal and task level. Results indicate regular differences associated with different task characteristics in several search behaviors, including task completion time, decision time (the time taken to decide whether a document is useful or not), and eye fixations, etc. We suggest these behaviors can be used as implicit indicators of the users task type.

Reinventing the inbox: supporting the management of pending tasks in email

Email was originally designed as a tool for asynchronous communication. However, its current usage goes far beyond that. One of the most commonly performed activities in email is the management of pending tasks. This research focuses on how to support this activity in email and explores alternative solutions that use different external representations of messages and associated tasks.

Email task management styles: the cleaners and the keepers

Email has become overloaded as users make use of email tools for performing a wide range of activities. Previous studies have demonstrated the different strategies employed by email users to manage messages. However, we have little information regarding how to explain those differences between users.The research described in this paper seeks to gain understanding of individual differences in email behaviour. We present results from a questionnaire-based study, which focused on how email users dealt with messages that relate to future tasks or events. We identified two types of user, defined by how they dealt with such messages: the cleaners and the keepers. The difference between these two groups can be attributed to differences in email experience and requirements for flexibility of closure. The ultimate goal of such research is to be able to predict differences in email use and to inform email user interface design and we discuss possible ways in which this could be done.

Task and user effects on reading patterns in information search

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.

Discriminating meta-search: a framework for evaluation

There was a proliferation of electronic information sources and search engines in the 1990s. Many of these information sources became available through the ubiquitous interface of the Web browser. Diverse information sources became accessible to information professionals and casual end users alike. Much of the information was also hyperlinked, so that information could be explored by browsing as well as searching. While vast amounts of information were now just a few keystrokes and mouseclicks away, as the choices multiplied, so did the complexity of choosing where and how to look for the electronic information. Much of the complexity in information exploration at the turn of the twenty-first century arose because there was no common cataloguing and control system across the various electronic information sources. In addition, the many search engines available diAered widely in terms of their domain coverage, query methods and eAciency. Meta-search engines were developed to improve search performance by querying multiple search engines at once. In principle, meta-search engines could greatly simplify the search for electronic information by selecting a subset of first-level search engines and digital libraries to submit a query to based on the characteristics of the user, the query/topic, and the search strategy. This selection would be guided by diagnostic knowledge about which of the first-level search engines works best under what circumstances. Programmatic research is required to develop this diagnostic knowledge about first-level search engine performance. This paper introduces an evaluative framework for this type of research and illustrates its use in two experiments. The experimental results obtained are used to characterize some properties of leading search engines (as of 1998). Significant interactions were observed between search engine and two other factors (time of day and Web domain). These findings supplement those of earlier studies, providing preliminary information about the complex relationship between search engine functionality and performance in diAerent contexts. While the specific results obtained represent a time-dependent Information Processing and Management 35 (1999) 337‐362

Inferring user knowledge level from eye movement patterns

The acquisition of information and the search interaction process is influenced strongly by a persons use of their knowledge of the domain and the task. In this paper we show that a users level of domain knowledge can be inferred from their interactive search behaviors without considering the content of queries or documents. A technique is presented to model a users information acquisition process during search using only measurements of eye movement patterns. In a user study (n=40) of search in the domain of genomics, a representation of the participants domain knowledge was constructed using self-ratings of knowledge of genomics-related terms (n=409). Cognitive effort features associated with reading eye movement patterns were calculated for each reading instance during the search tasks. The results show correlations between the cognitive effort due to reading and an individuals level of domain knowledge. We construct exploratory regression models that suggest it is possible to build models that can make predictions of the users level of knowledge based on real-time measurements of eye movement patterns during a task session.