Welcome to the InfoSci Platform
Reference Hub3
Predicting Search Intent Based on In-Search Context for Exploratory Search

Predicting Search Intent Based on In-Search Context for Exploratory Search

Vikram Singh
Copyright: © 2019 |Volume: 11 |Issue: 3 |Pages: 23
ISSN: 1937-965X|EISSN: 1937-9668|EISBN13: 9781522564966|DOI: 10.4018/IJAPUC.2019070104
Cite Article Cite Article

MLA

Singh, Vikram. "Predicting Search Intent Based on In-Search Context for Exploratory Search." IJAPUC vol.11, no.3 2019: pp.53-75. http://doi.org/10.4018/IJAPUC.2019070104

APA

Singh, V. (2019). Predicting Search Intent Based on In-Search Context for Exploratory Search. International Journal of Advanced Pervasive and Ubiquitous Computing (IJAPUC), 11(3), 53-75. http://doi.org/10.4018/IJAPUC.2019070104

Chicago

Singh, Vikram. "Predicting Search Intent Based on In-Search Context for Exploratory Search," International Journal of Advanced Pervasive and Ubiquitous Computing (IJAPUC) 11, no.3: 53-75. http://doi.org/10.4018/IJAPUC.2019070104

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

Modern information systems are expected to assist users with diverse goals, via exploiting the topical dimension (‘what' the user is searching for) of information needs. However, the intent dimension (‘why' the user is searching) has preferred relatively lesser for the same intention. 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 proposes a novel intent estimation strategy; based on the intuition that captured intent, and proactively extracts likely results. The captured ‘Pre-search' context adapts query term proximities within matched results beside document-term statistics and pseudo-relevance feedback with user-relevance feedback for In-search. The assessment asserts the superior performance of the proposed strategy over the equivalent on tradeoffs, e.g., novelty, diversity (coverage, topicality), retrieval (precision, recall, F-measure) and exploitation vs exploration.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.