The next-item recommendation has been in the central of interest in real-world applications such as e-commerce. However, it is challenging to infer what a user may purchase next due the complex interactions in the historical sessions and the changing semantics of an item over time. Most existing methods employ separate models to generate the general preference and the sequential patterns for the next-item recommendation without considering the interactions between the two factors or use a simple linear combination of the two factors. In this paper, we propose a deep adaptable co-embedding neural network (ACENet) to address these limitations. ACENet not only adaptably balances the combination of general preference and sequential patterns but also introduces dynamic attention for each factor in hybrid representations. Extensive experiments on two real-world datasets show the superiority of ACENet compared with other state-of-the-art methods.

中文翻译：

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具有深度自适应共嵌入神经网络的下一项推荐

下一项推荐一直是电子商务等实际应用程序的核心。然而，由于历史会话中的复杂交互以及项目语义随时间的变化，推断用户接下来可能购买什么是具有挑战性的。大多数现有方法使用单独的模型来生成下一个项目推荐的一般偏好和序列模式，而不考虑两个因素之间的相互作用或使用两个因素的简单线性组合。在本文中，我们提出了一种深度自适应共嵌入神经网络 (ACENet) 来解决这些限制。ACENet 不仅适应性地平衡了一般偏好和序列模式的组合，而且还为混合表示中的每个因素引入了动态注意力。