  Click-through data has been used in various ways in Web search such as
estimating relevance between documents and queries. Since only search snippets
are perceived by users before issuing any clicks, the relevance induced by
clicks are usually called \emph{perceived relevance} which has proven to be
quite useful for Web search. While there is plenty of click data for popular
queries, very little information is available for unpopular tail ones. These
tail queries take a large portion of the search volume but search accuracy for
these queries is usually unsatisfactory due to data sparseness such as limited
click information. In this paper, we study the problem of modeling perceived
relevance for queries without click-through data. Instead of relying on users'
click data, we carefully design a set of snippet features and use them to
approximately capture the perceived relevance. We study the effectiveness of
this set of snippet features in two settings: (1) predicting perceived
relevance and (2) enhancing search engine ranking. Experimental results show
that our proposed model is effective to predict the relative perceived
relevance of Web search results. Furthermore, our proposed snippet features are
effective to improve search accuracy for longer tail queries without
click-through data.
