Exploratory search: from finding to understanding
Communications of the ACM - Supporting exploratory search
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Complex tasks like answering research questions or solving problems require to carry out longitudinal processes where different information objects need to be gathered, collected, interpreted, analyzed, and evaluated [1]. Such a process normally includes several search and exploration sessions where the user interactively digs deeper into a more or less unknown domain. This research is driven by the fact that most common systems are designed to fit a general user where users are submitting queries and the retrieval system returns a ranked list of results. Regardless of the user, the query always returns the same list of results. Individual aspects like age, gender, profession or experience are often not taken into account, for example the difference in searching between children and adults. Unfortunately many systems are optimized for lookup searches, expecting that the user is only interested in facts and not in problem solving. Additionally common systems still assume that the user has a static information need which remains unchanged during the seeking process. In each step of the overall seeking process, the user faces a new situation in which knowledge and information need changes. This influences the relevance of information objects and may direct each user individually to different topics, domains, tasks or even search strategies. Due to uncertainty and missing knowledge, exploratory search activities need far more assistance like closing the the gap between different search sessions, allowing the user to review and continue their search more easily. Moreover the complexity of working tasks and the individual qualifications require personalized support to the searcher. The goal of this research is to investigate a concept assisting the user within such interactive exploratory search activities, allowing an effective information exploration by personalizing the seeking & searching process. Personalized IR systems need to adapt to relevant factors and commit itself to the specific user and the personal search behavior. The user should be guided throughout the searching process, suggesting useful search strategies and effective tactics which matches the users searching behavior and the current situation. To bridge different search sessions, past activities must be visualized in a kind of breadcrumb or timeline. That's why we are currently prototyping a way to visualize the personal Google search history using Timeline JS. To further assist the user with strategic search support, it is necessary to be aware of the user herself and specific contextual circumstances which may be relevant to the situation. General information about the user like gender or age but also relevance feedback can be fetched explicitly, allowing the system to adapt in a more coarse grained way (e.g. deciding the way of presenting results). Moreover integrating common used applications (e.g. Evernote) or providing other ways to let the user manage tasks will help to understand the goal of the search activities. For this reason we are currently investigating ways to link search activities with task management. Information about the search behavior and indirectly the users knowledge and expertise can be conveyed by logging (e.g. query logs) and examining system interactions. The fetched data should be made transparent to the user, showing what kind of information has been gathered so far. The implicitly gained information has to be refined with the explicit ones and also other contextual data collected tacitly from different interfaces or sensors (e.g. time, location). Bringing it all together will allow a more fine grained way of system adaption and offers new options in assisting the user during the long-term search activities showing personalized search strategies and possible next search steps appropriate to the information need and level of experience.