Entity Recommendation for Everyday Digital Tasks
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Entity Recommendation for Everyday Digital Tasks. / Jacucci, Giulio; Daee, Pedram; Vuong, Tung; Andolina, Salvatore; Klouche, Khalil; Sjöberg, Mats; Ruotsalo, Tuukka; Kaski, Samuel.
In: ACM Transactions on Computer-Human Interaction, Vol. 28, No. 5, 3458919, 2021.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Entity Recommendation for Everyday Digital Tasks
AU - Jacucci, Giulio
AU - Daee, Pedram
AU - Vuong, Tung
AU - Andolina, Salvatore
AU - Klouche, Khalil
AU - Sjöberg, Mats
AU - Ruotsalo, Tuukka
AU - Kaski, Samuel
N1 - Publisher Copyright: © 2021 Copyright held by the owner/author(s). Publication rights licensed to ACM.
PY - 2021
Y1 - 2021
N2 - 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 real-world 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.
AB - 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 real-world 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.
KW - Proactive search
KW - user intent modeling
UR - http://www.scopus.com/inward/record.url?scp=85114459665&partnerID=8YFLogxK
U2 - 10.1145/3458919
DO - 10.1145/3458919
M3 - Journal article
AN - SCOPUS:85114459665
VL - 28
JO - ACM Transactions on Computer-Human Interaction
JF - ACM Transactions on Computer-Human Interaction
SN - 1073-0516
IS - 5
M1 - 3458919
ER -
ID: 281810576