论文标题
déjàvu：一个下文化的时间注意机制，用于顺序推荐
Déjà vu: A Contextualized Temporal Attention Mechanism for Sequential Recommendation
论文作者
论文摘要
根据用户在历史上的顺序行为来预测用户的偏好对于现代推荐系统而言至关重要。大多数现有的顺序推荐算法都集中在顺序动作之间的过渡结构上，但是在对历史事件对当前预测的影响进行建模时，很大程度上忽略了时间和上下文信息。 在本文中，我们认为过去事件对用户当前操作的影响在随着时间的流逝和不同的情况下应有所不同。因此，我们提出了一种上下文化的时间关注机制，该机制学会权衡历史行为的影响，不仅对它是什么行动，而且对何时以及如何发生行动的影响。更具体地说，要动态地校准自我发挥机制的相对输入依赖性，我们部署了多个参数化的内核函数来学习各种时间动力学，然后使用上下文信息来确定每个输入都遵循这些重新拨打的内核。在两个大型公共建议数据集的经验评估中，我们的模型始终优于一组最新的顺序推荐方法。
Predicting users' preferences based on their sequential behaviors in history is challenging and crucial for modern recommender systems. Most existing sequential recommendation algorithms focus on transitional structure among the sequential actions, but largely ignore the temporal and context information, when modeling the influence of a historical event to current prediction. In this paper, we argue that the influence from the past events on a user's current action should vary over the course of time and under different context. Thus, we propose a Contextualized Temporal Attention Mechanism that learns to weigh historical actions' influence on not only what action it is, but also when and how the action took place. More specifically, to dynamically calibrate the relative input dependence from the self-attention mechanism, we deploy multiple parameterized kernel functions to learn various temporal dynamics, and then use the context information to determine which of these reweighing kernels to follow for each input. In empirical evaluations on two large public recommendation datasets, our model consistently outperformed an extensive set of state-of-the-art sequential recommendation methods.
