  Traditional machine-learned ranking systems for web search are often trained
to capture stationary relevance of documents to queries, which has limited
ability to track non-stationary user intention in a timely manner. In recency
search, for instance, the relevance of documents to a query on breaking news
often changes significantly over time, requiring effective adaptation to user
intention. In this paper, we focus on recency search and study a number of
algorithms to improve ranking results by leveraging user click feedback. Our
contributions are three-fold. First, we use real search sessions collected in a
random exploration bucket for \emph{reliable} offline evaluation of these
algorithms, which provides an unbiased comparison across algorithms without
online bucket tests. Second, we propose a re-ranking approach to improve search
results for recency queries using user clicks. Third, our empirical comparison
of a dozen algorithms on real-life search data suggests importance of a few
algorithmic choices in these applications, including generalization across
different query-document pairs, specialization to popular queries, and
real-time adaptation of user clicks.
