Correlating pre-search and in-search context to predict search intent for exploratory search
by Vikram Singh
International Journal of Business Intelligence and Data Mining (IJBIDM), Vol. 20, No. 3, 2022

Abstract: Modern information systems are expected to respond to a wide variety of information needs from users with diverse goals. The topical dimension ('what' the user is searching for) of these information needs is well studied; however, the intent dimension ('why' the user is searching) has received relatively less attention. Traditionally, the intent is an immediate reason, purpose, or goal that motivates the user search, and captured in search contexts (pre-search, in-search, pro-search). An ideal information system would be able to use. This article proposed a novel intent estimation strategy, based on the intuition that captured intent proactively extracts potential results. Captured pre-search context adapts query term proximities within matched results beside document-terms statistics and pseudo-relevance feedback with user-relevance feedback for in-search. The assessment asserts the superior performance of the proposed strategy over equivalent on trade-offs, e.g., novelty, diversity (coverage, topicality), retrieval (precision, recall, F-measure) and exploitation vs. exploration.

Online publication date: Mon, 11-Apr-2022

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