In context-aware recommendation systems, most existing methods encode users’ preferences by mapping item and category information into the same space, which is just a stack of information. The item and category information contained in the interaction behaviours is not fully utilized. Moreover, since users’ preferences for a candidate item are influenced by the changes in temporal and historical behaviours, it is unreasonable to predict correlations between users and candidates by using users’ fixed features. A fine-grained and coarse-grained information based framework proposed in our paper which considers multi-granularity information of users’ historical behaviours. First, a parallel structure is provided to mine users’ preference information under different granularities. Then, self-attention and attention mechanisms are used to capture the dynamic preferences. Experiment results on two publicly available datasets show that our framework outperforms state-of-the-art methods across the calculated evaluation metrics.

中文翻译：

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将细粒度和粗粒度信息考虑为上下文感知推荐

在感知上下文的推荐系统中，大多数现有方法都是通过将项目和类别信息映射到同一空间（仅是一堆信息）中来编码用户的偏好。交互行为中包含的项目和类别信息没有得到充分利用。而且，由于用户对候选项目的偏好受时间和历史行为的变化影响，因此通过使用用户的固定特征来预测用户与候选者之间的相关性是不合理的。本文提出了一种基于细粒度和粗粒度信息的框架，该框架考虑了用户历史行为的多粒度信息。首先，提供了一个并行结构来挖掘不同粒度下的用户偏好信息。然后，自我注意和注意机制用于捕获动态偏好。在两个可公开获得的数据集上的实验结果表明，在计算出的评估指标上，我们的框架优于最新方法。