  One of the core problems in large-scale recommendations is to retrieve top
relevant candidates accurately and efficiently, preferably in sub-linear time.
Previous approaches are mostly based on a two-step procedure: first learn an
inner-product model, and then use some approximate nearest neighbor (ANN)
search algorithm to find top candidates. In this paper, we present Deep
Retrieval (DR), to learn a retrievable structure directly with user-item
interaction data (e.g. clicks) without resorting to the Euclidean space
assumption in ANN algorithms. DR's structure encodes all candidate items into a
discrete latent space. Those latent codes for the candidates are model
parameters and learnt together with other neural network parameters to maximize
the same objective function. With the model learnt, a beam search over the
structure is performed to retrieve the top candidates for reranking.
Empirically, we first demonstrate that DR, with sub-linear computational
complexity, can achieve almost the same accuracy as the brute-force baseline on
two public datasets. Moreover, we show that, in a live production
recommendation system, a deployed DR approach significantly outperforms a
well-tuned ANN baseline in terms of engagement metrics. To the best of our
knowledge, DR is among the first non-ANN algorithms successfully deployed at
the scale of hundreds of millions of items for industrial recommendation
systems.
