Entity Recommendation for Everyday Digital Tasks

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dc.contributor.author Jacucci, Giulio
dc.contributor.author Daee, Pedram
dc.contributor.author Vuong, Tung
dc.contributor.author Andolina, Salvatore
dc.contributor.author Klouche, Khalil
dc.contributor.author Sjöberg, Mats
dc.contributor.author Ruotsalo, Tuukka
dc.contributor.author Kaski, Samuel
dc.date.accessioned 2021-09-16T04:22:02Z
dc.date.available 2021-09-16T04:22:02Z
dc.date.issued 2021-10
dc.identifier.citation Jacucci , G , Daee , P , Vuong , T , Andolina , S , Klouche , K , Sjöberg , M , Ruotsalo , T & Kaski , S 2021 , ' Entity Recommendation for Everyday Digital Tasks ' , ACM Transactions on Computer - Human Interaction , vol. 28 , no. 5 , 29 . https://doi.org/10.1145/3458919
dc.identifier.other PURE: 168504198
dc.identifier.other PURE UUID: e9ce91f2-54f5-41f6-b2a9-df0f91568c7c
dc.identifier.other WOS: 000693409700001
dc.identifier.other ORCID: /0000-0003-3282-5878/work/100083844
dc.identifier.other ORCID: /0000-0002-3317-3421/work/100084508
dc.identifier.uri http://hdl.handle.net/10138/334390
dc.description.abstract 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 monitoring, (2) introducing entity-based computing and interaction, and (3) investigating the previously overlooked usefulness of entity recommendations and their actual impact on user behavior in real tasks. The methodology exploits context from screen frames recorded every 2 seconds to recommend information entities related to the current task. We embodied this methodology in an interactive system and investigated the relevance and influence of the recommended entities in a study with participants resuming their realworld tasks after a 14-day monitoring phase. Results show that the recommendations allowed participants to find more relevant entities than in a control without the system. In addition, the recommended entities were also used in the actual tasks. In the discussion, we reflect on a research agenda for entity recommendation in context, revisiting comprehensive monitoring to include the physical world, considering entities as actionable recommendations, capturing drifting intent and routines, and considering explainability and transparency of recommendations, ethics, and ownership of data. en
dc.format.extent 41
dc.language.iso eng
dc.relation.ispartof ACM Transactions on Computer - Human Interaction
dc.rights unspecified
dc.rights.uri info:eu-repo/semantics/openAccess
dc.subject Proactive search
dc.subject user intent modeling
dc.subject RELEVANCE FEEDBACK
dc.subject INFORMATION
dc.subject SEARCH
dc.subject 113 Computer and information sciences
dc.title Entity Recommendation for Everyday Digital Tasks en
dc.type Article
dc.contributor.organization Department of Computer Science
dc.contributor.organization Ubiquitous Interaction research group / Giulio Jacucci
dc.contributor.organization Helsinki Institute for Information Technology
dc.contributor.organization Finnish Center for Artificial Intelligence (FCAI)
dc.description.reviewstatus Peer reviewed
dc.relation.doi https://doi.org/10.1145/3458919
dc.relation.issn 1073-0516
dc.rights.accesslevel openAccess
dc.type.version publishedVersion

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