User intention is an important factor to be considered for recommender systems, which always changes dynamically in different contexts. Recent studies (represented by sequential recommendation) begin to focus on predicting what users want beyond what users like, which are better at capturing user intention and have attracted a surge of interest. However, user intention modeling is non-trivial, because it is generally influenced by various factors, among which item relations and their temporal evolutionary effects are of great importance. For example, consumption of a cellphone will have varying impacts on the demands for its relational items: For complements, the demands are likely to be promoted in the short term; while for substitutes, the long-term effect may take advantage, because users do not need another cellphone immediately. Moreover, the temporal evolutions of different relational effects vary across different domains, which makes it challenging to adaptively take them into consideration. As a result, most existing studies only loosely incorporate item relations by encoding their semantics into embeddings, neglecting fine-grained time-aware effects. In this work, we propose Knowledge-aware Dynamic Attention (KDA) to take both relational effects and their temporal evolutions into consideration. Specifically, to model dynamic impacts of historical relational interactions on user intention, we aggregate the history sequence into relation-specific embeddings, where the attention weight consists of two parts. First, we measure the relational intensity between historical items and the target item to model the absolute degree of influence in terms of each relation. Second, to model how the relational effects drift with time, we innovatively introduce Fourier transform with learnable frequency-domain embeddings to estimate temporal decay functions of different relations adaptively. Subsequently, the self-attention mechanism is leveraged to derive the final representation of the whole history sequence, which reflects the dynamic user intention and will be applied to generate the recommendation list. Extensive experiments in three real-world datasets indicate the proposed KDA model significantly outperforms the state-of-the-art methods on the Top- K recommendation task. Moreover, the proposed Fourier-based method opens up a new avenue to adaptively integrate temporal dynamics into general neural models.

中文翻译：

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迈向动态用户意图

用户意图是推荐系统需要考虑的一个重要因素，推荐系统总是在不同的上下文中动态变化。最近的研究（以顺序推荐为代表）开始专注于预测用户想要的东西，而不是用户喜欢的东西，这更能捕捉用户意图并引起了极大的兴趣。然而，用户意图建模并非易事，因为它通常受到各种因素的影响，其中项目关系及其时间演化效应非常重要。例如，手机的消费对其相关物品的需求会产生不同的影响：对于补充品，需求可能会在短期内得到提升；而对于替代品来说，长期效应可能会发挥作用，因为用户不需要立即换一部手机。而且，不同关系效应的时间演变在不同领域有所不同，这使得自适应地考虑它们具有挑战性。因此，大多数现有研究只是通过将它们的语义编码到嵌入中来松散地结合项目关系，而忽略了细粒度​​的时间感知效应。在这项工作中，我们提出知识感知动态注意 (KDA) 以同时考虑关系效应及其时间演变。具体来说，为了模拟历史关系交互对用户意图的动态影响，我们将历史序列聚合到特定于关系的嵌入中，其中注意力权重由两部分组成。首先，我们测量历史项目和目标项目之间的关系强度，以根据每个关系对影响的绝对程度进行建模。第二，为了模拟关系效应如何随时间漂移，我们创新地引入了具有可学习频域嵌入的傅里叶变换，以自适应地估计不同关系的时间衰减函数。随后，利用自注意力机制推导出整个历史序列的最终表示，它反映了动态的用户意图，并将用于生成推荐列表。在三个真实世界数据集中的大量实验表明，所提出的 KDA 模型显着优于 Top--of-the-art 方法。利用自注意力机制得出整个历史序列的最终表示，它反映了动态的用户意图，并将用于生成推荐列表。在三个真实世界数据集中的大量实验表明，所提出的 KDA 模型显着优于 Top--of-the-art 方法。利用自注意力机制得出整个历史序列的最终表示，它反映了动态的用户意图，并将用于生成推荐列表。在三个真实世界数据集中的大量实验表明，所提出的 KDA 模型显着优于 Top--of-the-art 方法。ķ推荐任务。此外，所提出的基于傅立叶的方法开辟了一条新的途径，可以将时间动力学自适应地集成到一般的神经模型中。