  Despite the somewhat different techniques used in developing search engines
and recommender systems, they both follow the same goal: helping people to get
the information they need at the right time. Due to this common goal, search
and recommendation models can potentially benefit from each other. The recent
advances in neural network technologies make them effective and easily
extendable for various tasks, including retrieval and recommendation. This
raises the possibility of jointly modeling and optimizing search ranking and
recommendation algorithms, with potential benefits to both. In this paper, we
present theoretical and practical reasons to motivate joint modeling of search
and recommendation as a research direction. We propose a general framework that
simultaneously learns a retrieval model and a recommendation model by
optimizing a joint loss function. Our preliminary results on a dataset of
product data indicate that the proposed joint modeling substantially
outperforms the retrieval and recommendation models trained independently. We
list a number of future directions for this line of research that can
potentially lead to development of state-of-the-art search and recommendation
models.
