  Sponsored search optimizes revenue and relevance, which is estimated by
Revenue Per Mille (RPM). Existing sponsored search models are all based on
traditional statistical models, which have poor RPM performance when queries
follow a heavy-tailed distribution. Here, we propose an RPM-oriented Query
Rewriting Framework (RQRF) which outputs related bid keywords that can yield
high RPM. RQRF embeds both queries and bid keywords to vectors in the same
implicit space, converting the rewriting probability between each query and
keyword to the distance between the two vectors. For label construction, we
propose an RPM-oriented sample construction method, labeling keywords based on
whether or not they can lead to high RPM. Extensive experiments are conducted
to evaluate performance of RQRF. In a one month large-scale real-world traffic
of e-commerce sponsored search system, the proposed model significantly
outperforms traditional baseline.
