双边状态空间模型用于非随机多模态评论反馈的序列推荐
原标题：Two-Sided State-Space Models for Sequential Recommendation with Non-Random Multimodal Review Feedback
AI 摘要
本文提出了一种用于序列推荐的双边状态空间模型（TS-SSM），该模型同时建模用户和物品的动态状态，并利用多模态评论的缺失模式、局部图传播以及正负反馈的不对称结转效应来提升推荐性能。在六个亚马逊类别和Goodreads Fantasy数据集上的实验表明，TS-SSM在Recall@20指标上显著优于现有方法。
正文节选
Two-Sided State-Space Models for Sequential Recommendation with Non-Random Multimodal Review Feedback Abstract Two-sided digital platforms are inherently dynamic: user preferences shift, item popularity evolves, and reviews both reflect and drive these changes. Yet most sequential recommendation systems treat reviews as passive signals for updating user states, leaving two aspects underexplored. First, review generation is nonrandom, depending on evolving latent states of both users and items. S