Exploratory search requires the system to assist the user in comprehending the information space and expressing evolving search intents for iterative exploration and retrieval of information. We introduce interactive intent modeling, a technique that models a user's evolving search intents and visualizes them as keywords for interaction. The user can provide feedback on the keywords, from which the system learns and visualizes an improved intent estimate and retrieves information. We report experiments comparing variants of a system implementing interactive intent modeling to a control system. Data comprising search logs, interaction logs, essay answers, and questionnaires indicate significant improvements in task performance, information retrieval performance over the session, information comprehension performance, and user experience. The improvements in retrieval effectiveness can be attributed to the intent modeling and the effect on users' task performance, breadth of information comprehension, and user experience are shown to be dependent on a richer visualization. Our results demonstrate the utility of combining interactive modeling of search intentions with interactive visualization of the models that can benefit both directing the exploratory search process and making sense of the information space. Our findings can help design personalized systems that support exploratory information seeking and discovery of novel information.

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Since the recent emergence of electronic literature resources, researchers have begun to adopt new informationseeking practices. The purpose of this research is to investigate the information needs and searching behaviors of researchers, and their implications for electronic literature search tools. We conducted mixed-method case studies involving interviews, diary logs, and observations of computer scientists followed by a web-based survey to validate our findings. The results show that computer science researchers have the following main purposes for seeking information: keeping up to date, exploring new topics, reviewing literature, collaborating, preparing lectures, and recommending material for students. We found that keeping up to date with research is the most frequent purpose and exploring unfamiliar research areas is the most difficult. Furthermore, we found that literature searching is a collaborative process and, depending on the search purpose, different information sources and navigation strategies are used. On the basis of these findings we discuss six design challenges for literature search tools, which are: providing support for keeping up to date with research, exploring unfamiliar topics, browsing user history, collaborating and sharing, performing a federated search that goes beyond scholarly research, and sorting and navigating the results.

Term-Relevance Prediction from Brain Signals (TRPB) is proposed to automatically detect relevance of text information directly from brain signals. An experiment with forty participants was conducted to record neural activity of participants while providing relevance judgments to text stimuli for a given topic. High-precision scientific equipment was used to quantify neural activity across 32 electroencephalography (EEG) channels. A classifier based on a multi-view EEG feature representation showed improvement up to 17% in relevance prediction based on brain signals alone. Relevance was also associated with brain activity with significant changes in certain brain areas. Consequently, TRPB is based on changes identified in specific brain areas and does not require user-specific training or calibration. Hence, relevance predictions can be conducted for unseen content and unseen participants. As an application of TRPB we demonstrate a high-precision variant of the classifier that constructs sets of relevant terms for a given unknown topic of interest. Our research shows that detecting relevance from brain signals is possible and allows the acquisition of relevance judgments without a need to observe any other user interaction. This suggests that TRPB could be used in combination or as an alternative for conventional implicit feedback signals, such as dwell time or click-through activity.