  Aggregated search aims to construct search result pages (SERPs) from
blue-links and heterogeneous modules (such as news, images, and videos).
Existing studies have largely ignored the correlations between blue-links and
heterogeneous modules when selecting the heterogeneous modules to be presented.
We observe that the top ranked blue-links, which we refer to as the
\emph{context}, can provide important information about query intent and helps
identify the relevant heterogeneous modules. For example, informative terms
like "streamed" and "recorded" in the context imply that a video module may
better satisfy the query. To model and utilize the context information for
aggregated search, we propose a model with context attention and representation
learning (CARL). Our model applies a recurrent neural network with an attention
mechanism to encode the context, and incorporates the encoded context
information into module embeddings. The context-aware module embeddings
together with the ranking policy are jointly optimized under the Markov
decision process (MDP) formulation. To achieve a more effective joint learning,
we further propose an optimization function with self-supervision loss to
provide auxiliary supervision signals. Experimental results based on two public
datasets demonstrate the superiority of CARL over multiple baseline approaches,
and confirm the effectiveness of the proposed optimization function in boosting
the joint learning process.
