Visual Re-Ranking for Multi-Aspect Information Retrieval
We present visual re-ranking, an interactive visualization technique for multi-aspect information retrieval. In multi-aspect search, the information need of the user consists of more than one aspect or query simultaneously. While visualization and interactive search user interface techniques for improving user interpretation of search results have been proposed, the current research lacks understanding on how useful these are for the user: whether they lead to quantifiable benefits in perceiving the result space and allow faster, and more precise retrieval. Our technique visualizes relevance and document density on a two-dimensional map with respect to the query phrases. Pointing to a locat…
InspirationWall
Collaborative idea generation leverages social interactions and knowledge sharing to spark diverse associations and produce creative ideas. Information exploration systems expand the current context by suggesting novel but related concepts. In this paper we introduce InspirationWall, an unobtrusive display that leverages speech recognition and information exploration to enhance an ongoing idea generation session with automatically retrieved concepts that relate to the conversation. We evaluated the system in six idea generation sessions of 20 minutes with small groups of two people. Preliminary results suggest that InspirationWall contrasts the decay of idea productivity over time and can t…
Does More Context Help? Effects of Context Window and Application Source on Retrieval Performance
We study the effect of contextual information obtained from a user’s digital trace on Web search performance. Contextual information is modeled using Dirichlet–Hawkes processes (DHP) and used in augmenting Web search queries. The context is captured by monitoring all naturally occurring user behavior using continuous 24/7 recordings of the screen and associating the context with the queries issued by the users. We report a field study in which 13 participants installed a screen recording and digital activity monitoring system on their laptops for 14 days, resulting in data on all Web search queries and the associated context data. A query augmentation (QAug) model was built to expand the or…
Querytogether : Enabling entity-centric exploration in multi-device collaborative search
Collaborative and co-located information access is becoming increasingly common. However, fairly little attention has been devoted to the design of ubiquitous computing approaches for spontaneous exploration of large information spaces enabling co-located collaboration. We investigate whether an entity-based user interface provides a solution to support co-located search on heterogeneous devices. We present the design and implementation of QueryTogether, a multi-device collaborative search tool through which entities such as people, documents, and keywords can be used to compose queries that can be shared to a public screen or specific users with easy touch enabled interaction. We conducted…
Investigating Proactive Search Support in Conversations
Conversations among people involve solving disputes, building common ground, and reinforce mutual beliefs and assumptions. Conversations often require external information that can support these human activities. In this paper, we study how a spoken conversation can be supported by a proactive search agent that listens to the conversation, detects entities mentioned in the conversation, and proactively retrieves and presents information related to the conversation. A total of 24 participants (12 pairs) were involved in informal conversations, using either the proactive search agent or a control condition that did not support conversational analysis or proactive information retrieval. Data c…
Spoken conversational context improves query auto-completion in web search
Web searches often originate from conversations in which people engage before they perform a search. Therefore, conversations can be a valuable source of context with which to support the search process. We investigate whether spoken input from conversations can be used as a context to improve query auto-completion. We model the temporal dynamics of the spoken conversational context preceding queries and use these models to re-rank the query auto-completion suggestions. Data were collected from a controlled experiment and comprised conversations among 12 participant pairs conversing about movies or traveling. Search query logs during the conversations were recorded and temporally associated…
SearchBot: Supporting voice conversations with proactive search
Searching during conversations and social interactions is becoming increasingly common. Although searching could be helpful for solving arguments, building common ground, and reinforcing mutual assumptions, it can also cause inter-actional problems. Proactive search approaches can enrich conversations with additional information without neglecting the shared and established social norms of being attentive to ongoing interaction. This demo showcases SearchBot, a tool that minimizes the issues associated with the practice of searching during conversations. It accomplishes this by tracking conversational background speech and then providing continuous recommendations of related documents and e…
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 compr…
Entity Recommendation for Everyday Digital Tasks
| openaire: EC/H2020/826266/EU//CO-ADAPT Recommender systems can support everyday digital tasks by retrieving and recommending useful information contextually. This is becoming increasingly relevant in services and operating systems. Previous research often focuses on specific recommendation tasks with data captured from interactions with an individual application. The quality of recommendations is also often evaluated addressing only computational measures of accuracy, without investigating the usefulness of recommendations in realistic tasks. The aim of this work is to synthesize the research in this area through a novel approach by (1) demonstrating comprehensive digital activity monitor…
IntentStreams
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 refine…
EntityBot: Supporting Everyday Digital Tasks with Entity Recommendations
Everyday digital tasks can highly benefit from systems that recommend the right information to use at the right time. However, existing solutions typically support only specific applications and tasks. In this demo, we showcase EntityBot, a system that captures context across application boundaries and recommends information entities related to the current task. The user’s digital activity is continuously monitored by capturing all content on the computer screen using optical character recognition. This includes all applications and services being used and specific to individuals’ computer usages such as instant messaging, emailing, web browsing, and word processing. A linear model is then …
EntityBot: Actionable Entity Recommendations for Everyday Digital Task
Our everyday digital tasks require access to information from a wide range of applications and systems. Although traditional search systems can help find information, they usually operate within one application (e.g., email client or web browser) and require the user's cognitive effort and attention to formulate proper search queries. In this paper, we demonstrate EntityBot, a system that proactively provides useful and supporting entities across application boundaries without requiring explicit query formulation. Our methodology is to exploit the context from screen frames captured every 2 seconds to recommend relevant entities for the current task. Recommendations are not restricted to on…
Flexible entity search on surfaces
Surface computing allows flexible search interaction where users can manipulate the representation of entities recommended for them to create new queries or augment existing queries by taking advantage of increased screen estate and almost physical tactile interaction. We demonstrate a search system based on 1) Direct Manipulation of Entity Representation on Surfaces and 2) Entity Recommendation and Document Retrieval. Entities are modeled as a knowledge-graph and the relevances of entities are computed using the graph structure. Users can manipulate the representation of entities via spatial grouping and assigning preferences on entities. Our contribution can help to design effective infor…