  Emergence of various vertical search engines highlights the fact that a
single ranking technology cannot deal with the complexity and scale of search
problems. For example, technology behind video and image search is very
different from general web search. Their ranking functions share few features.
Question answering websites (e.g., Yahoo! Answer) can make use of text matching
and click features developed for general web, but they have unique page
structures and rich user feedback, e.g., thumbs up and thumbs down ratings in
Yahoo! answer, which greatly benefit their own ranking. Even for those features
shared by answer and general web, the correlation between features and
relevance could be very different. Therefore, dedicated functions are needed in
order to better rank documents within individual domains. These dedicated
functions are defined on distinct feature spaces. However, having one search
box for each domain, is neither efficient nor scalable. Rather than typing the
same query two times into both Yahoo! Search and Yahoo! Answer and retrieving
two ranking lists, we would prefer putting it only once but receiving a
comprehensive list of documents from both domains on the subject. This
situation calls for new technology that blends documents from different sources
into a single ranking list. Despite the content richness of the blended list,
it has to be sorted by relevance none the less. We call such technology
blending, which is the main subject of this paper.
