Sequential recommender systems have demonstrated a huge success for next-item recommendation by explicitly exploiting the temporal order of users' historical interactions. In practice, user interactions contain more useful temporal information beyond order, as shown by some pioneering studies. In this paper, we systematically investigate various temporal information for sequential recommendation and identify three types of advantageous temporal patterns beyond order, including absolute time information, relative item time intervals and relative recommendation time intervals. We are the first to explore item-oriented absolute time patterns. While existing models consider only one or two of these three patterns, we propose a novel holistic temporal pattern based neural network, named HTP, to fully leverage all these three patterns. In particular, we introduce novel components to address the subtle correlations between relative item time intervals and relative recommendation time intervals, which render a major technical challenge. Extensive experiments on three real-world benchmark datasets show that our HTP model consistently and substantially outperforms many state-of-the-art models. Our code is publically available at https://github.com/623851394/HTP/tree/main/HTP-main

翻译：序列推荐系统通过显式利用用户历史交互的时间顺序，在下一项推荐任务中取得了巨大成功。实际上，用户交互中包含比顺序更有用的时间信息，一些开创性研究已表明这一点。本文系统性地研究了序列推荐中的各种时间信息，并识别出三种超越顺序的有益时间模式，包括绝对时间信息、相对项目时间间隔和相对推荐时间间隔。我们是首个探索面向项目的绝对时间模式的工作。现有模型仅考虑这三种模式中的一种或两种，而我们提出了一种新颖的基于整体时间模式的神经网络，命名为HTP，以充分利用所有这三种模式。特别地，我们引入了新型组件来处理相对项目时间间隔与相对推荐时间间隔之间的微妙关联，这构成了主要的技术挑战。在三个真实世界基准数据集上的大量实验表明，我们的HTP模型持续且显著优于许多最先进模型。我们的代码已在https://github.com/623851394/HTP/tree/main/HTP-main公开发布。