Given a sequence of sets, where each set has a timestamp and contains an arbitrary number of elements, temporal sets prediction aims to predict the elements in the subsequent set. Previous studies for temporal sets prediction mainly focus on the modelling of elements and implicitly represent each user's preference based on his/her interacted elements. However, user preferences are often continuously evolving and the evolutionary trend cannot be fully captured with the indirect learning paradigm of user preferences. To this end, we propose a continuous-time user preference modelling framework for temporal sets prediction, which explicitly models the evolving preference of each user by maintaining a memory bank to store the states of all the users and elements. Specifically, we first construct a universal sequence by arranging all the user-set interactions in a non-descending temporal order, and then chronologically learn from each user-set interaction. For each interaction, we continuously update the memories of the related user and elements based on their currently encoded messages and past memories. Moreover, we present a personalized user behavior learning module to discover user-specific characteristics based on each user's historical sequence, which aggregates the previously interacted elements from dual perspectives according to the user and elements. Finally, we develop a set-batch algorithm to improve the model efficiency, which can create time-consistent batches in advance and achieve 3.5x and 3.0x speedups in the training and evaluation process on average. Experiments on four real-world datasets demonstrate the superiority of our approach over state-of-the-arts under both transductive and inductive settings. The good interpretability of our method is also shown.

翻译：给定一系列集合，每个集合带有时间戳并包含任意数量的元素，时序集合预测旨在预测后续集合中的元素。以往针对时序集合预测的研究主要侧重于元素建模，并基于用户交互过的元素隐式表示其偏好。然而，用户偏好通常持续演化，且间接学习用户偏好的范式无法完整捕捉其演化趋势。为此，我们提出一种用于时序集合预测的连续时间用户偏好建模框架，该框架通过维护存储所有用户与元素状态的内存库，显式建模每个用户动态演化的偏好。具体而言，我们首先将所有用户-集合交互按时间非降序排列构建全局序列，随后按时间顺序从每个用户-集合交互中学习。对于每次交互，我们根据当前编码信息与历史记忆持续更新相关用户及元素的内存。此外，我们提出个性化用户行为学习模块，基于每个用户的历史序列发现其特有特征，该模块根据用户与元素从双重角度聚合历史交互过的元素。最后，我们开发集合批次算法提升模型效率，可预先创建时间一致批次，在训练与评估过程中平均实现3.5倍与3.0倍的加速。在四个真实数据集上的实验表明，我们的方法在直推式与归纳式设置下均优于现有最先进方法。同时展示了该方法良好的可解释性。