Entity Recommendation for Everyday Digital Tasks

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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

Title: Entity Recommendation for Everyday Digital Tasks
Author: Jacucci, Giulio; Daee, Pedram; Vuong, Tung; Andolina, Salvatore; Klouche, Khalil; Sjöberg, Mats; Ruotsalo, Tuukka; Kaski, Samuel
Contributor organization: Department of Computer Science
Ubiquitous Interaction research group / Giulio Jacucci
Helsinki Institute for Information Technology
Finnish Center for Artificial Intelligence (FCAI)
Date: 2021-10
Language: eng
Number of pages: 41
Belongs to series: ACM Transactions on Computer - Human Interaction
ISSN: 1073-0516
DOI: https://doi.org/10.1145/3458919
URI: http://hdl.handle.net/10138/334390
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.
Subject: Proactive search
user intent modeling
113 Computer and information sciences
Peer reviewed: Yes
Rights: unspecified
Usage restriction: openAccess
Self-archived version: publishedVersion

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