针对短视频推荐的时效性与兴趣动态变化，提出多聚合时变异构图网络。
Multi-Aggregator Time-Warping Heterogeneous Graph Neural Network for Personalized Micro-Video Recommendation
- 设计多聚合机制捕捉用户多维度偏好
- 引入时间扭曲建模用户兴趣动态演化
- 适合追求高时效性的短视频个性化推荐场景
短视频推荐正成为全球广泛关注的日常服务。基于图神经网络的推荐方法在多种任务中表现优异，但现有工作未能充分考虑短视频的特性，如新闻类内容的高时效性及用户兴趣的频繁变化。本文提出一种新型多聚合时变异构图神经网络（MTHGNN），用于基于序列会话的个性化新闻类短视频推荐。该模型综合研究短视频特征，通过多聚合机制挖掘用户偏好，捕捉用户兴趣的时序动态变化，并融入时效性考量。与当前最优方法对比，实验结果验证了MTHGNN模型的有效性。
原文摘要 · Abstract (English)
Micro-video recommendation is attracting global attention and becoming a popular daily service for people of all ages. Recently, Graph Neural Networks-based micro-video recommendation has displayed performance improvement for many kinds of recommendation tasks. However, the existing works fail to fully consider the characteristics of micro-videos, such as the high timeliness of news nature micro-video recommendation and sequential interactions of frequently changed interests. In this paper, a novel Multi-aggregator Time-warping Heterogeneous Graph Neural Network (MTHGNN) is proposed for personalized news nature micro-video recommendation based on sequential sessions, where characteristics of micro-videos are comprehensively studied, users' preference is mined via multi-aggregator, the temporal and dynamic changes of users' preference are captured, and timeliness is considered. Through the comparison with the state-of-the-arts, the experimental results validate the superiority of our MTHGNN model.
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