Why People Search for Images using Web Search Engines

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. We show that user intents in image search can be grouped into three classes: Explore/Learn, Entertain, and Locate/Acquire. Our lab-based user study reveals different user behavior patterns under these three intents, such as first click time, query reformulation, dwell time and mouse movement on the result page. Based on user interaction features during the early stages of an image search session, that is, before mouse scroll, we develop an intent classifier that is able to achieve promising results for classifying intents into our three intent classes. Given that all features can be obtained online and unobtrusively, the predicted intents can provide guidance for choosing ranking methods immediately after scrolling.