计算机科学 ›› 2023, Vol. 50 ›› Issue (6A): 220100066-8.doi: 10.11896/jsjkx.220100066
郑麟1, 林艺璇1, 周东霖1, 朱福喜2
ZHENG Lin1, LIN Yixuan1, ZHOU Donglin1, ZHU Fuxi2
摘要: 序列推荐研究近年来在推荐领域中发展迅速,已有的序列推荐方法善于捕捉用户的时序行为来实现偏好预测。其中,一些先进的方法融入用户的情感信息来引导行为挖掘。然而,先进的基于情感的序列推荐模型未考虑对多类别的用户情感序列进行关联挖掘;并且,这类方法无法直观地解释时序情感对用户偏好的贡献。为了弥补上述方法的局限,本工作首次尝试以记忆体的形式存储时序情感并对其施加约束。具体地,文中提出了情感自我约束和情感相互约束两种机制,来挖掘多类别情感之间的关联并辅助用户行为完成序列推荐。进一步地,提出的记忆框架能记录用户的时序情感注意力,从而在准确预测用户时序偏好的基础上提供一定程度的直观解释。实验结果表明,所提方法的性能优于先进的序列推荐方法,并且比基于情感的序列推荐模型具有更好的可解释效果。
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