Search results for "user intent modeling"

showing 3 items of 3 documents

Entity Recommendation for Everyday Digital Tasks

2021

| 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…

ExploitSettore INF/01 - InformaticaINFORMATIONComputer sciencemedia_common.quotation_subjectRelevance feedbackContext (language use)02 engineering and technologyTransparency (human–computer interaction)Recommender system113 Computer and information sciencesData scienceHuman-Computer InteractionTask (computing)user intent modelingRELEVANCE FEEDBACK020204 information systemsSEARCH0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingRelevance (information retrieval)Quality (business)Proactive searchmedia_common
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EntityBot: Supporting Everyday Digital Tasks with Entity Recommendations

2021

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 …

Settore INF/01 - InformaticaComputer science05 social sciencesWord processingContext (computing)User satisfactionLinear model02 engineering and technologyOptical character recognitionRecommender systemcomputer.software_genreTask (project management)Human–computer interactionUser intent modeling020204 information systems0202 electrical engineering electronic engineering information engineering0501 psychology and cognitive sciencesWeb navigationcomputerProactive information retrieval050107 human factorsReal-world tasks
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EntityBot: Actionable Entity Recommendations for Everyday Digital Task

2022

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…

user intent modelingSettore INF/01 - Informaticareal-world tasksProactive information retrievalCHI Conference on Human Factors in Computing Systems Extended Abstracts
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