Search

Users interact with digital systems with some task in his mind. An example of a task could be writing a research paper on a topic. Tasks can be single or multi-staged. In the process of accomplishing their task objectives, a user often needs to interact with an information retrieval (IR) system to address one or more information needs which arise while working on their task, e.g. for writing their research paper on a chosen topic, the user needs to look for existing research works related to the topic. Traditional IR systems do not take into account a user's task intent while showing search results to the user for a specific query submitted by the user. In our work we propose next generation IR systems (i.e. proactive IR systems) which seek to anticipate the user's underlying task from his interaction with a digital system to automatically identify their information needs and to suggest potentially relevant information sources to help the user to accomplish his task.

openaire: EC/H2020/826266/EU//CO-ADAPT 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.

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.

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.

Users of current search systems actively interact with the system to complete their search task. This can encompass formulating and reformulating a series queries expressing evolving of different information needs. We believe that the next generation of search systems will see a shift towards proactive understanding of user intent based on analysis of user activities. Such a proactive search system could start recommending documents that are likely to help users accomplish their tasks without requiring them to explicitly submit queries to the system. We propose a framework to evaluate such a search system. The key idea behind our proposed metric is to aggregate a correlation measure over a search session between the expected outcome, which in this case refers to the list of documents retrieved with a true user query, and the predicted outcome, which refers to the list of documents recommended by a proactive search system. Experiments on the AOL query log data show that the ranking of two sample proactive IR systems induced by our metric conforms to the expected ranking between these systems.

Conversations among people involve solving disputes, building common ground, and reinforce mutual beliefs and assumptions. Conversations often require external information that can support these human activities. In this paper, we study how a spoken conversation can be supported by a proactive search agent that listens to the conversation, detects entities mentioned in the conversation, and proactively retrieves and presents information related to the conversation. A total of 24 participants (12 pairs) were involved in informal conversations, using either the proactive search agent or a control condition that did not support conversational analysis or proactive information retrieval. Data comprising transcripts, interaction logs, questionnaires, and interviews indicated that the proactive search agent effectively augmented the conversations, affected the conversations' topical structure, and reduced the need for explicit search activity. The findings also revealed key challenges in the design of proactive search systems that assist people in natural conversations.

Searching during conversations and social interactions is becoming increasingly common. Although searching could be helpful for solving arguments, building common ground, and reinforcing mutual assumptions, it can also cause inter-actional problems. Proactive search approaches can enrich conversations with additional information without neglecting the shared and established social norms of being attentive to ongoing interaction. This demo showcases SearchBot, a tool that minimizes the issues associated with the practice of searching during conversations. It accomplishes this by tracking conversational background speech and then providing continuous recommendations of related documents and entities in a non-intrusive way [3].

Background In the last 20 years, Brazil has undergone dramatic changes in terms of socioeconomic development and health care. In the first decade of the 2000s, the Ministry of Health (MoH) developed a series of programs focused on reducing infant mortality, including the Family Health Program as a national policy for primary care. In this paper, we propose a method to correct underreporting of deaths and live births. After vital statistics are corrected, infant mortality trends are analyzed for the period 2000–2010 by macro-geographical region. Methods A proactive search of live births and deaths was carried out in the Amazon and Northeast regions in 2010 to find vital events that occurred in 2008 and were not reported to the Ministry of Health. The probabilistic sample of 133 municipalities was stratified by adequacy of vital information reporting. For each municipality, the adequacy analysis was based on the reported age-standardized mortality rate per 1,000 population and the ratio between reported and estimated live births. Correction factors were estimated by strata based on additional vital events found in the proactive search. The procedure was generalized to correct municipal vital statistics for the period 2000–2010. Results In the proactive search, 35% of non-reported deaths were found within the health system (hospitals and other health establishments), but 28% were found in non-official sources, like illegal cemeteries. In areas of extreme poverty and unreliable vital information, the estimated completeness of infant death reporting was only 33%. After correction of vital information, the estimated infant mortality rate decreased from 26.1 in 2000 to 16.0 in 2010, with an annual rate of decrease of 4.7%, greater than the required rate to achieve the Millennium Development Goal. Among Brazilian regions, the Northeast showed the largest decrease, from 38.4 to 20.1 per 1,000 live births. Conclusions The proactive search for vital events was shown to be a good strategy both in terms of understanding local irregularities and for correcting vital statistics. The methodology could be applied in other countries to routinely assess the pattern and extent of birth and death under-registration in order to improve the utility of these data to inform health policies.