  Domain expertise is regarded as one of the key factors impacting search
success: experts are known to write more effective queries, to select the right
results on the result page, and to find answers satisfying their information
needs. Search transaction logs play the crucial role in the result ranking. Yet
despite the variety in expertise levels of users, all prior interactions are
treated alike, suggesting that weighting in expertise can improve the ranking
for informational tasks. The main aim of this paper is to investigate the
impact of high levels of technical domain expertise on both search behavior and
task outcome. We conduct an online user study with searchers proficient in
programming languages. We focus on Java and Javascript, yet we believe that our
study and results are applicable for other expertise-sensitive search tasks.
The main findings are three-fold: First, we constructed expertise tests that
effectively measure technical domain expertise and correlate well with the
self-reported expertise. Second, we showed that there is a clear position bias,
but technical domain experts were less affected by position bias. Third, we
found that general expertise helped finding the correct answers, but the domain
experts were more successful as they managed to detect better answers. Our work
is using explicit tests to determine user expertise levels, which is an
important step toward fully automatic detection of expertise levels based on
interaction behavior. A deeper understanding of the impact of expertise on
search behavior and task outcome can enable more effective use of expert
behavior in search logs - essentially make everyone search as an expert.
