Recent years have witnessed extensive researches on developing two tower recommendation models for relieving information overload. Four building modules can be identified in such models, namely, user-item encoding, negative sampling, loss computing and back-propagation updating. To the best of our knowledge, existing algorithms have researched only on the first three modules, yet neglecting the backpropagation module. They all adopt a kind of two backpropagation strategy, which are based on an implicit assumption of equally treating users and items in the training phase. In this paper, we challenge such an equal training assumption and propose a novel one backpropagation updating strategy, which keeps the normal gradient backpropagation for the item encoding tower, but cuts off the backpropagation for the user encoding tower. Instead, we propose a moving-aggregation updating strategy to update a user encoding in each training epoch. Except the proposed backpropagation updating module, we implement the other three modules with the most straightforward choices. Experiments on four public datasets validate the effectiveness and efficiency of our model in terms of improved recommendation performance and reduced computation overload over the state-of-the-art competitors.

中文翻译：

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两塔推荐模型中的一种反向传播

近年来，人们对开发两个塔式推荐模型以缓解信息过载进行了广泛的研究。在此类模型中可以识别四个构建模块，即用户项编码、负采样、损失计算和反向传播更新。据我们所知，现有算法仅研究了前三个模块，而忽略了反向传播模块。它们都采用一种两种反向传播策略，该策略基于在训练阶段平等对待用户和项目的隐含假设。在本文中，我们挑战了这种平等的训练假设，并提出了一种新颖的反向传播更新策略，该策略保持项目编码塔的正常梯度反向传播，但切断用户编码塔的反向传播。相反，我们提出了一种移动聚合更新策略来更新每个训练时期的用户编码。除了提出的反向传播更新模块外，我们用最直接的选择实现了其他三个模块。对四个公共数据集的实验验证了我们的模型在改进推荐性能和减少计算过载方面比最先进的竞争对手的有效性和效率。