【摘要】 协同过滤作为推荐系统核心技术,面临严重的评分数据稀疏性问题.融合物品文本信息可以有效的解决数据稀疏性问题,然而,目前的方法侧重于提取文本的单维特征,忽略了物品语义表示的多维特性.深度挖掘物品内容的多维特性可以更加精细化描述物品的语义信息,有助于提升推荐效果.为此,本文提出基于胶囊网络的概率生成模型.模型利用胶囊网络挖掘文本的多维语义特征,并以正则化方式融入概率矩阵分解框架,建立用户与物品之间的内在关系.实验结果表明本文提出的模型具有更高的评分预测精度.更多还原

【Abstract】 Collaborative filtering,as the core technology of recommendation systems,is currently facing the sparsity problem of rating data.This can be effectively solved through integrating item text information.However,current methods focus on extracting the one-dimensional features of the text,neglecting its multidimensional semantic features.Digging deeply into the multidimensional semantic features of the text can improve the recommendations.To help achieve this goal,a probabilistic matrix factorization model based on multidimensional semantic representation learning is proposed in the present study.The model uses a capsule network to mine the multidimensional semantic features of the text,and then integrates it into the probabilistic matrix decomposition framework using the regularization method to reveal hidden features linking users and items.Experimental results show that the proposed model has higher prediction accuracy.更多还原