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Koshman, Sherry; Spink, Amanda; Jansen, Bernard J.

doi:10.1002/asi.v57:14

Cited by 70 publications

(1 citation statement)

References 227 publications

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“…The special journal publication edited by Jones and Purves [90] is a useful reference on the issues of geographical variables. The papers on implicit relevance feedback by Kelly and Belkin [95,96]; Kelly and Fu [97]; Kelly et al [98]; Kelly [92,93,94] are definitely worth reading. The survey by Lalmas and Ruthven [107] provides a precise, recent and exhaustive account of relevance feedback.…”

Section: Concluding Remarks and Suggestionsmentioning

confidence: 99%

Contextual Search: A Computational Framework

Melucci¹

2012

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The growing availability of data in electronic form, the expansion of the World Wide Web (WWW) and the accessibility of computational methods for large-scale data processing have allowed researchers in Information Retrieval (IR) to design systems which can effectively and efficiently constrain search within the boundaries given by context, thus transforming classical search into contextual search. Because of the constraints imposed by context, contextual search better focuses on the user's relevance and improves retrieval performance, since the out-of-context aspects of the search carried out by users that are likely linked to irrelevant documents are left apart.This survey introduces contextual search within a computational framework based on contextual variables, contextual factors and statistical models. The framework adopted in this survey considers Query IntentQuery: from Latin quaerere, "ask, seek." Intent: from Latin intendere ("intend," "direct"), from in-"toward" + tendere "stretch," "tend." Oxford Dictionary

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