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 -