Interactive Intent Modeling for Exploratory Search

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.Peer reviewe

Knowledge-Context in search systems: Toward information-literate actions

In this perspectives paper we define knowledge-context as meta information that searchers use when making sense of information displayed in and accessible from a search engine results page (SERP). We argue that enriching the knowledge-context in SERPs has great potential for facilitating human learning, critical thinking, and creativity by expanding searchers’ information-literate actions such as comparing, evaluating, and differentiating between information sources. Thus it supports the development of learning-centric search systems. Using theories and empirical findings from psychology and the learning sciences, we first discuss general effects of Web search on memory and learning. After reviewing selected research addressing metacognition and self-regulated learning, we discuss design goals for search systems that support metacognitive skills required for long-term learning, creativity, and critical thinking. We then propose that SERPs make both bibliographic and inferential knowledge-context readily accessible to motivate and facilitate information-literate actions for learning and creative endeavors. A brief discussion of related ideas, designs, and prototypes found in prior work follows. We conclude the paper by presenting future research directions and questions on knowledge-context, information-literate actions, and learning-centric search systems.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/148270/1/Smith and Rieh Knowledge-Context in Search Systems CHIIR2019.pd

Directing and Combining Multiple Queries for Exploratory Search by Visual Interactive Intent Modeling

In interactive information-seeking, a user often performs many interrelated queries and interactions covering multiple aspects of a broad topic of interest. Especially in difficult information-seeking tasks the user may need to find what is in common among such multiple aspects. Therefore, the user may need to compare and combine results across queries. While methods to combine queries or rankings have been proposed, little attention has been paid to interactive support for combining multiple queries in exploratory search. We introduce an interactive information retrieval system for exploratory search with multiple simultaneous search queries that can be combined. The user is able to direct search in the multiple queries, and combine queries by two operations: intersection and difference, which reveal what is relevant to the user intent of two queries, and what is relevant to one but not the other. Search is directed by relevance feedback on visualized user intent models of each query. Operations on queries act directly on the intent models inferring a combined user intent model. Each combination yields a new result (ranking) and acts as a new search that can be interactively directed and further combined. User experiments on difficult information-seeking tasks show that our novel system with query operations yields more relevant top-ranked documents in a shorter time than a baseline multiple-query system.Peer reviewe

Interactive faceted query suggestion for exploratory search : Whole-session effectiveness and interaction engagement

Abstract The outcome of exploratory information retrieval is not only dependent on the effectiveness of individual responses to a set of queries, but also on relevant information retrieved during the entire exploratory search session. We study the effect of search assistance, operationalized as an interactive faceted query suggestion, for both whole-session effectiveness and engagement through interactive faceted query suggestion. A user experiment is reported, where users performed exploratory search tasks, comparing interactive faceted query suggestion and a control condition with only conventional typed-query interaction. Data comprised of interaction and search logs show that the availability of interactive faceted query suggestion substantially improves whole-session effectiveness by increasing recall without sacrificing precision. The increased engagement with interactive faceted query suggestion is targeted to direct situated navigation around the initial query scope, but is not found to improve individual queries on average. The results imply that research in exploratory search should focus on measuring and designing tools that engage users with directed situated navigation support for improving whole-session performance.Peer reviewe

Supporting interactive summarization for explainable exploratory search

Exploratory search is characterised by user uncertainty with respect to search domain and information seeking goals. This uncertainty can negatively impact users’ abilities to assess the quality of search results, causing them to scroll through more documents than necessary and struggle to give consistent relevance feedback. As users’ information needs are assumed to be highly dynamic and expected to evolve over time, successful searches can be indistinguishable from those that have drifted erroneously away from their original search intent. Indeed, given their lack of domain knowledge, searchers may be slow, or even unable, to recognise when search results have become skewed towards another topic. With these issues in mind, we designed and implemented an interactive search system which integrated a keyword summaries algorithm, Exploratory Search Captions (ESC) to support users in exploratory search. This thesis investigated into the usefulness of ESC in terms of user experience, user behaviour and also explored impact of design decision in terms of user satisfaction. We evaluated the ESC system with a user study in the context of exploratory search of scientific literature in Computer Science. According to the user study results, participants almost unanimously preferred the retrieval system that incorporated ESC; and the presence of captions dramatically impacts user behaviour: users issue more queries, investigate fewer documents per query, but see more documents overall. We demonstrated the usefulness of ESC, the improved usability of ESC system, and the positive impact of our design decisions

EYE-AS-AN-INPUT FOR IMPROVING INTERACTIVE INFORMATION RETRIEVAL

In this work, Publication Access Through Tiered Interaction and Exploration (PATTIE) is presented with the eye as an additional input modality. PATTIE is built upon the scatter/gather information retrieval paradigm where users can explore a visual and interactive table-of-contents metaphor for large-scale document collections in an iterative manner. Additionally, the prototype has been integrated with eye-tracking through the web camera and experimental findings are provided to demonstrate a proof-of-concept for interest modeling at the term level and implicit relevance feedback on the gold standard inaugural 2019 Text REtrieval Conference Precision Medicine dataset (TREC PM). Low error rates for gaze tracking, and acceptable performance on binary classification of interest are reported as well as statistically significant increases in precision and recall performance for relevant information on a TREC PM task when PATTIE is used with eye-as-an-input versus a baseline PATTIE system.Doctor of Philosoph