Browsing by Author "Vreeken, Jilles"

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Is exploratory search different? A comparison of information search behavior for exploratory and lookup tasks
(2016-11-01) Athukorala, Kumaripaba; Głowacka, Dorota; Jacucci, Giulio; Oulasvirta, Antti; Vreeken, Jilles
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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.
Reconstructing an epidemic over time
(2016-08-13) Rozenshtein, Polina; Gionis, Aristides; Prakash, B. Aditya; Vreeken, Jilles
A4 Artikkeli konferenssijulkaisussa
We consider the problem of reconstructing an epidemic over time, or, more general, reconstructing the propagation of an activity in a network. Our input consists of a temporal network, which contains information about when two nodes interacted, and a sample of nodes that have been reported as infected. The goal is to recover the flow of the spread, including discovering the starting nodes, and identifying other likely-infected nodes that are not reported. The problem we consider has multiple applications, from public health to social media and viral marketing purposes. Previous work explicitly factor-in many unrealistic assumptions: it is assumed that (a) the underlying network does not change; (b) we have access to perfect noise-free data; or (c) we know the exact propagation model. In contrast, we avoid these simplifications: we take into account the temporal network, we require only a small sample of reported infections, and we do not make any restrictive assumptions about the propagation model. We develop CulT, a scalable and effective algorithm to reconstruct epidemics that is also suited for online settings. CulT works by formulating the problem as that of a temporal Steiner-tree computation, for which we design a fast algorithm leveraging the specific problem structure. We demonstrate the effcacy of the proposed approach through extensive experiments on diverse datasets.