使用BERT预训练语言模型获取评论文本的向量表示，通过Bi-GRU网络学习其中的语义特征，分别采用情感权重和注意力机制来为评论向量分配权重，动态调节其对用户特征和产品特征的影响程度，并以加权求和的方式获得用户特征和产品特征，最后利用DeepFM算法对用户特征和产品特征进行深度交互，预测产品推荐的评分值。[结果]在Amazon product data数据集上的实验结果表明，本文模型比基线模型的RMSE与MAE指标最多可降低24.43%和31.44%；使用情感权重为用户评论加权的方法相比注意力机制，在RMSE与MAE指标上最多可降低2.59%和3.89%。
☆17Mar 28, 2023Updated 3 years ago
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