Search (4 results, page 1 of 1)

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Abstract

Exploratory search is an increasingly important activity yet challenging for users. Although there exists an ample amount of research into understanding exploration, most of the major information retrieval (IR) systems do not provide tailored and adaptive support for such tasks. One reason is the lack of empirical knowledge on how to distinguish exploratory and lookup search behaviors in IR systems. The goal of this article is to investigate how to separate the 2 types of tasks in an IR system using easily measurable behaviors. In this article, we first review characteristics of exploratory search behavior. We then report on a controlled study of 6 search tasks with 3 exploratory-comparison, knowledge acquisition, planning-and 3 lookup tasks-fact-finding, navigational, question answering. The results are encouraging, showing that IR systems can distinguish the 2 search categories in the course of a search session. The most distinctive indicators that characterize exploratory search behaviors are query length, maximum scroll depth, and task completion time. However, 2 tasks are borderline and exhibit mixed characteristics. We assess the applicability of this finding by reporting on several classification experiments. Our results have valuable implications for designing tailored and adaptive IR systems.

Date

18.10.2016 13:52:29

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Abstract

Understanding users' search behavior has largely relied on the information available from search engine logs, which provide limited information about the contextual factors affecting users' behavior. Consequently, questions such as how users' intentions, task goals, and substances of the users' tasks affect search behavior, as well as what triggers information needs, remain largely unanswered. We report an experiment in which naturalistic information search behavior was captured by analyzing 24/7 continuous recordings of information on participants' computer screens. Written task diaries describing the participants' tasks were collected and used as real-life task contexts for further categorization. All search tasks were extracted and classified under various task categories according to users' intentions, task goals, and substances of the tasks. We investigated the effect of different task categories on three behavioral factors: search efforts, content-triggers, and application context. Our results suggest four findings: (i) Search activity is integrally associated with the users' creative processes. The content users have seen prior to searching more often triggers search, and is used as a query, within creative tasks. (ii) Searching within intellectual and creative tasks is more time-intensive, while search activity occurring as a part of daily routine tasks is associated with more frequent searching within a search task. (iii) Searching is more often induced from utility applications in tasks demanding a degree of intellectual effort. (iv) Users' leisure information-seeking activity is occurring inherently within social media services or comes from social communication platforms. The implications of our findings for information access and management systems are discussed.

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Abstract

Previous research investigated how to leverage the new type of social data available on the web, e.g., tags, ratings and reviews, in recommending and personalizing information. However, previous works mainly focused on predicting ratings using collaborative filtering or quantifying personalized ranking quality in simulations. As a consequence, the effect of social information in user's information search and information-selection behavior remains elusive. The objective of our research is to investigate the effects of social information on users' interactive search and information-selection behavior. We present a computational method and a system implementation combining different graph overlays: social, personal and search-time user input that are visualized for the user to support interactive information search. We report on a controlled laboratory experiment, in which 24 users performed search tasks using three system variants with different graphs as overlays composed from the largest publicly available social content and review data from Yelp: personal preferences, tags combined with personal preferences, and tags and social ratings combined with personal preferences. Data comprising search logs, questionnaires, simulations, and eye-tracking recordings show that: 1) the search effectiveness is improved by using and visualizing the social rating information and the personal preference information as compared to content-based ranking. 2) The need to consult external information before selecting information is reduced by the presentation of the effects of different overlays on the search results. Search effectiveness improvements can be attributed to the use of social rating and personal preference overlays, which was also confirmed in a follow-up simulation study. With the proposed method we demonstrate that social information can be incorporated to the interactive search process by overlaying graphs representing different information sources. We show that the combination of social rating information and personal preference information improves search effectiveness and reduce the need to consult external information. Our method and findings can inform the design of interactive search systems that leverage the information available on the social web.

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Abstract

The use of implicit relevance feedback from neurophysiology could deliver effortless information retrieval. However, both computing neurophysiologic responses and retrieving documents are characterized by uncertainty because of noisy signals and incomplete or inconsistent representations of the data. We present the first-of-its-kind, fully integrated information retrieval system that makes use of online implicit relevance feedback generated from brain activity as measured through electroencephalography (EEG), and eye movements. The findings of the evaluation experiment (N = 16) show that we are able to compute online neurophysiology-based relevance feedback with performance significantly better than chance in complex data domains and realistic search tasks. We contribute by demonstrating how to integrate in interactive intent modeling this inherently noisy implicit relevance feedback combined with scarce explicit feedback. Although experimental measures of task performance did not allow us to demonstrate how the classification outcomes translated into search task performance, the experiment proved that our approach is able to generate relevance feedback from brain signals and eye movements in a realistic scenario, thus providing promising implications for future work in neuroadaptive information retrieval (IR).