Why People Search for Images using Web Search Engines
Xiaohui Xie, Yiqun Liu, Maarten de Rijke, Jiyin He, Min Zhang,, Shaoping Ma

TL;DR
This paper investigates the reasons behind human image search behavior, classifies user intents into three categories, and develops a classifier to predict user intent early in the search session, enhancing search engine responsiveness.
Contribution
It introduces a new intent classification framework for image search, based on user behavior analysis, and demonstrates effective early-stage intent prediction from unobtrusive interaction features.
Findings
User intents are categorized into Explore/Learn, Entertain, and Locate/Acquire.
Distinct user behavior patterns are identified for each intent category.
An intent classifier achieves promising accuracy using early interaction features.
Abstract
What are the intents or goals behind human interactions with image search engines? Knowing why people search for images is of major concern to Web image search engines because user satisfaction may vary as intent varies. Previous analyses of image search behavior have mostly been query-based, focusing on what images people search for, rather than intent-based, that is, why people search for images. To date, there is no thorough investigation of how different image search intents affect users' search behavior. In this paper, we address the following questions: (1)Why do people search for images in text-based Web image search systems? (2)How does image search behavior change with user intent? (3)Can we predict user intent effectively from interactions during the early stages of a search session? To this end, we conduct both a lab-based user study and a commercial search log analysis.…
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Taxonomy
TopicsAdvanced Image and Video Retrieval Techniques · Image Retrieval and Classification Techniques · Information Retrieval and Search Behavior
