Interactive Intent Modeling for Exploratory Search

Abstract

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.

Video summarization using event‐related potential responses to shot boundaries in real‐time video watching

“…In cases where a user's ERP data show highly positive activation over the fronto‐central cortex (for instance, the P600 effect), the system can classify the corresponding image as relevant. The image can then automatically be added as another query image, thus relieving the user from the laborious task of providing feedback (Ruotsalo, Jacucci, Myllymäki, & Kaski, ). We anticipate that our proposed method can be used to investigate searching and searching behavior tasks (Mostafa & Gwizdka, ).…”

Interactive visual data exploration with subjective feedback: an information-theoretic approach

“…However, as these approaches are not targeted at data exploration, we do not review them here. Finally, several specialpurpose methods have been developed for visual iterative data exploration in specific contexts, e.g., for itemset mining and subgroup discovery [21], [22], [23], [24], information retrieval [25], and network analysis [26].…”

Proactive identification of query failure

“…In these studies, a query segment is defined as a search session segment which starts from one issued query and ends at the next query (Mitsui, Liu, Belkin, & Shah, ). Ruotsalo et al () represented users' interactive intents as keywords for interaction and developed an intent extraction and visualization technique to support search interactions. Rha, Mitsui, Belkin and Shah () empirically investigated the connections between search intentions and queries and found that users' search intentions in query segments are significantly associated with their query reformulation types.…”