2.5.6: Improving Web Searches
- Page ID
- 58602
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\(\newcommand{\avec}{\mathbf a}\) \(\newcommand{\bvec}{\mathbf b}\) \(\newcommand{\cvec}{\mathbf c}\) \(\newcommand{\dvec}{\mathbf d}\) \(\newcommand{\dtil}{\widetilde{\mathbf d}}\) \(\newcommand{\evec}{\mathbf e}\) \(\newcommand{\fvec}{\mathbf f}\) \(\newcommand{\nvec}{\mathbf n}\) \(\newcommand{\pvec}{\mathbf p}\) \(\newcommand{\qvec}{\mathbf q}\) \(\newcommand{\svec}{\mathbf s}\) \(\newcommand{\tvec}{\mathbf t}\) \(\newcommand{\uvec}{\mathbf u}\) \(\newcommand{\vvec}{\mathbf v}\) \(\newcommand{\wvec}{\mathbf w}\) \(\newcommand{\xvec}{\mathbf x}\) \(\newcommand{\yvec}{\mathbf y}\) \(\newcommand{\zvec}{\mathbf z}\) \(\newcommand{\rvec}{\mathbf r}\) \(\newcommand{\mvec}{\mathbf m}\) \(\newcommand{\zerovec}{\mathbf 0}\) \(\newcommand{\onevec}{\mathbf 1}\) \(\newcommand{\real}{\mathbb R}\) \(\newcommand{\twovec}[2]{\left[\begin{array}{r}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\ctwovec}[2]{\left[\begin{array}{c}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\threevec}[3]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\cthreevec}[3]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\fourvec}[4]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\cfourvec}[4]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\fivevec}[5]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\cfivevec}[5]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\mattwo}[4]{\left[\begin{array}{rr}#1 \amp #2 \\ #3 \amp #4 \\ \end{array}\right]}\) \(\newcommand{\laspan}[1]{\text{Span}\{#1\}}\) \(\newcommand{\bcal}{\cal B}\) \(\newcommand{\ccal}{\cal C}\) \(\newcommand{\scal}{\cal S}\) \(\newcommand{\wcal}{\cal W}\) \(\newcommand{\ecal}{\cal E}\) \(\newcommand{\coords}[2]{\left\{#1\right\}_{#2}}\) \(\newcommand{\gray}[1]{\color{gray}{#1}}\) \(\newcommand{\lgray}[1]{\color{lightgray}{#1}}\) \(\newcommand{\rank}{\operatorname{rank}}\) \(\newcommand{\row}{\text{Row}}\) \(\newcommand{\col}{\text{Col}}\) \(\renewcommand{\row}{\text{Row}}\) \(\newcommand{\nul}{\text{Nul}}\) \(\newcommand{\var}{\text{Var}}\) \(\newcommand{\corr}{\text{corr}}\) \(\newcommand{\len}[1]{\left|#1\right|}\) \(\newcommand{\bbar}{\overline{\bvec}}\) \(\newcommand{\bhat}{\widehat{\bvec}}\) \(\newcommand{\bperp}{\bvec^\perp}\) \(\newcommand{\xhat}{\widehat{\xvec}}\) \(\newcommand{\vhat}{\widehat{\vvec}}\) \(\newcommand{\uhat}{\widehat{\uvec}}\) \(\newcommand{\what}{\widehat{\wvec}}\) \(\newcommand{\Sighat}{\widehat{\Sigma}}\) \(\newcommand{\lt}{<}\) \(\newcommand{\gt}{>}\) \(\newcommand{\amp}{&}\) \(\definecolor{fillinmathshade}{gray}{0.9}\)Internet searches are designed to provide satisfactory results to the user. User satisfaction is usually defined in terms of whether the user finds the information that they were seeking. A major problem in measuring user satisfaction for web searches is that most users will be unwilling to provide direct feedback with each search. To aid with this problem a research study from 2005 considered implicit measures of user satisfaction, and whether they would provide a useful surrogate for directly measuring user satisfaction in web searches (Fox et al. 2005).
To determine if such implicit measures would be effective, the authors of the study examined the behavior of 146 internet users over a six-week period. Direct feedback was collected from these users on the satisfaction with individual search results as well as satisfaction with the entire search session. Through the use of a dialog box that popped up at the end of a search session, the user could select one response from a set of four possible responses: “I liked it,” “It was interesting but I need more information,” “I didn’t like it,” and “I did not get a chance to evaluate it.” The response “I liked it” was taken to mean that the user was satisfied with the search result. Implicit measures such as mouse and keyboard actions, whether the results were printed or were added to their favorites, and several other measures were recorded for each search as well. Sophisticated statistical modeling techniques where then used to determine if the implicit measures could be used to predict the direct feedback from the participants.
The researchers were able to find some interesting predictive results. For example, users who spent nearly a minute looking at a page that had abundant images and was in the top three results on the list were satisfied with the results nearly 90% of the time. Similarly, participants who spent less than a minute on a page, and exited the page were dissatisfied nearly 75% of the time.
This type of study is based on empiricism and the scientific method. The type of implicit measures that were selected for the study were based on previous observations in other contexts. The researchers used these propositions to form the empirical hypothesis that these activities would be useful in the current setting as well. The connection between the proposed implicit measures was then established by dividing the responses from the implicit measures in such a way that they predicted the outcome in the most efficient way. The high percentages associated with the results on the satisfaction scale then provide evidence that the implicit measures are useful in predicting the explicit measures. The researchers also took advantage of additional, more sophisticated methods to ensure that the results observed in the study were not simply due to chance but were likely due to authentic observed effects.