Browsing by Subject "user intent modeling"

Sort by: Order: Results:

Now showing items 1-3 of 3
  • Jacucci, Giulio; Daee, Pedram; Vuong, Tung; Andolina, Salvatore; Klouche, Khalil; Sjöberg, Mats; Ruotsalo, Tuukka; Kaski, Samuel (2021)
    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.
  • Ruotsalo, Tuukka; Peltonen, Jaakko; Eugster, Manuel J.A.; Glowacka, Dorota; Floréen, Patrik; Myllymäki, Petri; Jacucci, Giulio; Kaski, Samuel (2018)
    Exploratory search requires the system to assist the user in comprehending the information space and expressing evolving search intents for iterative exploration and retrieval of information. We introduce interactive intent modeling, a technique that models a user’s evolving search intents and visualizes them as keywords for interaction. The user can provide feedback on the keywords, from which the system learns and visualizes an improved intent estimate and retrieves information. We report experiments comparing variants of a system implementing interactive intent modeling to a control system. Data comprising search logs, interaction logs, essay answers, and questionnaires indicate significant improvements in task performance, information retrieval performance over the session, information comprehension performance, and user experience. The improvements in retrieval effectiveness can be attributed to the intent modeling and the effect on users’ task performance, breadth of information comprehension, and user experience are shown to be dependent on a richer visualization. Our results demonstrate the utility of combining interactive modeling of search intentions with interactive visualization of the models that can benefit both directing the exploratory search process and making sense of the information space. Our findings can help design personalized systems that support exploratory information seeking and discovery of novel information.
  • Koskela, Markus; Luukkonen, Petri; Ruotsalo, Tuukka; Sjöberg, Mats; Floréen, Patrik (2018)
    A significant fraction of information searches are motivated by the user's primary task. An ideal search engine would be able to use information captured from the primary task to proactively retrieve useful information. Previous work has shown that many information retrieval activities depend on the primary task in which the retrieved information is to be used, but fairly little research has been focusing on methods that automatically learn the informational intents from the primary task context. We study how the implicit primary task context can be used to model the user's search intent and to proactively retrieve relevant and useful information. Data comprising of logs from a user study, in which users are writing an essay, demonstrate that users' search intents can be captured from the task and relevant and useful information can be proactively retrieved. Data from simulations with several datasets of different complexity show that the proposed approach of using primary task context generalizes to a variety of data. Our findings have implications for the design of proactive search systems that can infer users' search intent implicitly by monitoring users' primary task activities.