  In recent years, \emph{search story}, a combined display with other organic
channels, has become a major source of user traffic on platforms such as
e-commerce search platforms, news feed platforms and web and image search
platforms. The recommended search story guides a user to identify her own
preference and personal intent, which subsequently influences the user's
real-time and long-term search behavior. %With such an increased importance of
search stories, As search stories become increasingly important, in this work,
we study the problem of personalized search story recommendation within a
search engine, which aims to suggest a search story relevant to both a search
keyword and an individual user's interest. To address the challenge of modeling
both immediate and future values of recommended search stories (i.e.,
cross-channel effect), for which conventional supervised learning framework is
not applicable, we resort to a Markov decision process and propose a deep
reinforcement learning architecture trained by both imitation learning and
reinforcement learning. We empirically demonstrate the effectiveness of our
proposed approach through extensive experiments on real-world data sets from
JD.com.
