Wang Dongjing, Zhang Xin, Xiang Zhengzhe, Yu Dongjin, Xu Guandong, Deng Shuiguang
IEEE Trans Cybern. 2022 Nov;52(11):11893-11905. doi: 10.1109/TCYB.2021.3077361. Epub 2022 Oct 17.
Recommender systems are important approaches for dealing with the information overload problem in the big data era, and various kinds of auxiliary information, including time and sequential information, can help improve the performance of retrieval and recommendation tasks. However, it is still a challenging problem how to fully exploit such information to achieve high-quality recommendation results and improve users' experience. In this work, we present a novel sequential recommendation model, called multivariate Hawkes process embedding with attention (MHPE-a), which combines a temporal point process with the attention mechanism to predict the items that the target user may interact with according to her/his historical records. Specifically, the proposed approach MHPE-a can model users' sequential patterns in their temporal interaction sequences accurately with a multivariate Hawkes process. Then, we perform an accurate sequential recommendation to satisfy target users' real-time requirements based on their preferences obtained with MHPE-a from their historical records. Especially, an attention mechanism is used to leverage users' long/short-term preferences adaptively to achieve an accurate sequential recommendation. Extensive experiments are conducted on two real-world datasets (lastfm and gowalla), and the results show that MHPE-a achieves better performance than state-of-the-art baselines.
推荐系统是大数据时代应对信息过载问题的重要方法，各种辅助信息，包括时间和序列信息，都有助于提高检索和推荐任务的性能。然而，如何充分利用这些信息以获得高质量的推荐结果并提升用户体验仍是一个具有挑战性的问题。在这项工作中，我们提出了一种新颖的序列推荐模型，称为带注意力机制的多元霍克斯过程嵌入（MHPE-a），它将时间点过程与注意力机制相结合，根据目标用户的历史记录预测其可能交互的项目。具体而言，所提出的方法MHPE-a可以使用多元霍克斯过程准确地对用户在其时间交互序列中的序列模式进行建模。然后，我们基于从历史记录中使用MHPE-a获得的目标用户偏好，进行准确的序列推荐以满足他们的实时需求。特别是，使用注意力机制自适应地利用用户的长期/短期偏好来实现准确的序列推荐。我们在两个真实世界数据集（lastfm和gowalla）上进行了广泛的实验，结果表明MHPE-a比现有最先进的基线方法具有更好的性能。
