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 maximum inner product search (MIPS) algorithms to search top candidates, leading to potential loss of retrieval accuracy. In this paper, we present Deep Retrieval (DR), an end-to-end learnable structure model for large-scale recommendations. DR encodes all candidates into a discrete latent space. Those latent codes for the candidates are model parameters and to be learnt together with other neural network parameters to maximize the same objective function. With the model learnt, a beam search over the latent codes is performed to retrieve the top candidates. Empirically, we showed that DR, with sub-linear computational complexity, can achieve almost the same accuracy as the brute-force baseline.

中文翻译：

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深度检索：用于大规模推荐的端到端可学习结构模型

大规模推荐的核心问题之一是准确有效地检索最相关的候选者，最好是在亚线性时间内。以前的方法大多基于两步过程：首先学习内积模型，然后使用最大内积搜索 (MIPS) 算法搜索最佳候选者，从而导致检索准确性的潜在损失。在本文中，我们提出了深度检索 (DR)，这是一种用于大规模推荐的端到端可学习结构模型。DR 将所有候选编码到一个离散的潜在空间中。这些候选的潜在代码是模型参数，需要与其他神经网络参数一起学习，以最大化相同的目标函数。学习模型后，对潜在代码执行波束搜索以检索最佳候选者。根据经验，