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

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

From Exploration to Sensemaking: an Interactive Exploratory Search System

exist to satisfy people's information needs. Many search systems have been designed for well-formed and explicit information needs but few for exploratory purposes. By exploration, we mean the information need is ambiguous initially and evolves during the search process. There are mainly two parts within the thesis. In the first part, we will develop an interactive exploratory search system for the arXiv database, an open e-print archive for scientific articles. The part is mostly based on an initial study, in which a search engine, Scinet, based on an intent estimation model is proposed. With this search engine, users can direct their exploration by giving feedbacks to the estimated search intents, which are represented by relevant keywords. Intents are visualized and arranged into a radial layout where the radius measures relevance and the angle measures similarity. Users can drag a keyword closer to the center to indicate higher relevance or click on a keyword to assign full relevance and then the retrieved documents will be updated accordingly. Compared to the initial search system, a mind-map functionality is also added as a new feature. With this mind-map, users can temporarily store the keywords or titles that they find interesting. To verify the interactive exploratory ability, we have designed and conducted a small-scale experiment based on the arXiv dataset. Particularly, the keywords for arXiv articles are extracted by an automatic keyword extraction algorithm since most of the arXiv articles do not provide keywords by the authors. For the second part, we investigate a potential novel functionality of the Scinet search system on a large database of scientific articles. The ability of the system to support information seeking was shown in previous publication. Here we propose that this system will also support sensemaking, namely, help users to make sense of the results. We suggest that this advantage arises because in Scinet, not only are intents estimated but also the relationships between them are indicated on the interface. In order to better support sensemaking, the new functionality is also added to the prototype system in the initial study. We believe that this search system will help people to better understand and interpret the search results

Intentstreams: Smart parallel search streams for branching exploratory search

The user's understanding of information needs and the information available in the data collection can evolve during an exploratory search session. Search systems tailored for well-defined narrow search tasks may be suboptimal for exploratory search where the user can sequentially refine the expressions of her information needs and explore alternative search directions. A major challenge for exploratory search systems design is how to support such behavior and expose the user to relevant yet novel information that can be difficult to discover by using conventional query formulation techniques. We introduce IntentStreams, a system for exploratory search that provides interactive query refinement mechanisms and parallel visualization of search streams. The system models each search stream via an intent model allowing rapid user feedback. The user interface allows swift initiation of alternative and parallel search streams by direct manipulation that does not require typing. A study with 13 participants shows that IntentStreams provides better support for branching behavior compared to a conventional search system

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

Designing for Exploratory Search on Touch Devices

Exploratory search confront users with challenges in expressing search intents as the current search interfaces require investigating result listings to identify search directions, iterative typing, and reformulating queries. We present the design of Exploration Wall, a touch-based search user interface that allows incremental exploration and sense-making of large information spaces by combining entity search, flexible use of result entities as query parameters, and spatial configuration of search streams that are visualized for interaction. Entities can be flexibly reused to modify and create new search streams, and manipulated to inspect their relationships with other entities. Data comprising of task-based experiments comparing Exploration Wall with conventional search user interface indicate that Exploration Wall achieves significantly improved recall for exploratory search tasks while preserving precision. Subjective feedback supports our design choices and indicates improved user satisfaction and engagement. Our findings can help to design user interfaces that can effectively support exploratory search on touch devices

Query Morphing: A Proximity-Based Approach for Data Exploration

We are living in age where large information in the form of structured and unstructured data is generated through social media, blogs, lab simulations, sensors etc. on daily basis. Due to this occurrences, acquisition of relevant information becomes a challenging task for humans. Fundamental understanding of complex schema and content is necessary for formulating data retrieval request. Therefore, instead of search, we need exploration in which a naïve user walks through the database and stops when satisfactory information is met. During this, a user iteratively transforms his search request in order to gain relevant information; morphing is an approach for generation of various transformation of input. We proposed ‘Query morphing’, an approach for query reformulation based on data exploration. Identified design concerns and implementation constraints are also discussed for the proposed approach

From Hyperlinks to Hypercues : Entity-Based Affordances for Fluid Information Exploration

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Topic-Relevance Map: Visualization for Improving Search Result Comprehension

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