  Interactions between search and recommendation have recently attracted
significant attention, and several studies have shown that many potential
applications involve with a joint problem of producing recommendations to users
with respect to a given query, termed $Collaborative$ $Retrieval$ (CR).
Successful algorithms designed for CR should be potentially flexible at dealing
with the sparsity challenges since the setup of collaborative retrieval
associates with a given $query$ $\times$ $user$ $\times$ $item$ tensor instead
of traditional $user$ $\times$ $item$ matrix. Recently, several works are
proposed to study CR task from users' perspective. In this paper, we aim to
sufficiently explore the sophisticated relationship of each $query$ $\times$
$user$ $\times$ $item$ triple from items' perspective. By integrating
item-based collaborative information for this joint task, we present an
alternative factorized model that could better evaluate the ranks of those
items with sparse information for the given query-user pair. In addition, we
suggest to employ a recently proposed scalable ranking learning algorithm,
namely BPR, to optimize the state-of-the-art approach, $Latent$ $Collaborative$
$Retrieval$ model, instead of the original learning algorithm. The experimental
results on two real-world datasets, (i.e. \emph{Last.fm}, \emph{Yelp}),
demonstrate the efficiency and effectiveness of our proposed approach.
