融入情感分析的多层交互深度推荐模型研究

A Deep Recommendation Model with Multi-Layer Interaction and Sentiment Analysis

【目的】针对传统推荐方法中仅依赖单一的用户评分来推断用户偏好,忽略情感态度对用户行为影响的问题,提出一种融入情感分析的多层交互深度推荐模型。【方法】利用BRET词向量表征评论文本,双向循环神经网络量化评论中的情感信息,根据情感分值更新评分矩阵,映射出用户与资源的浅层特征;结合卷积神经网络和自注意力机制从评论文本中捕获用户与资源的深层特征;融合浅层与深层特征,采用多层神经网络建模用户与资源间的非线性交互,预测资源推荐的评分值。【结果】在Amazon Product Data数据集上的实验结果表明,与其他基线模型对比,本文模型的MAE和RMSE指标最多下降7.93和9.73个百分点。【局限】未考虑用户情感的时间动态性,且忽略了情感分析方法的领域自适应性。【结论】融入情感分析的推荐模型能更准确地反映用户真实兴趣偏好,对比现有相似模型,本文所提模型能有效提升推荐质量。

[Objective]This paper proposes a deep recommendation model with multi-layer interaction and sentiment analysis.It tries to improve the traditional recommendation algorithms which rely on single user ratings to infer user preferences and ignore the impacts of sentiments.[Methods]First,we used the BRET word vector to represent the reviews,and utilized the bidirectional recurrent neural network to quantify their sentiments.Then,we updated the rating matrix using the sentiment values,and mapped the shallow features of users and resources.Fourth,we captured the deep features of users and resources from reviews with the convolutional neural network and the self-attention mechanism.Finally,we merged the shallow and deep features,and used the multi-layer perceptron to model the complex nonlinear interaction between users and resources to predict the rating of recommended resources.[Results]We examined the model with Amazon dataset and found the MAE and RMSE metrics were upto 7.93%and 9.37%lower than those of the baseline models.[Limitations]Our model did not include the temporal dynamics of user sentiment and ignore the domain adaptiveness of sentiment analysis methods.[Conclusions]The recommendation model incorporating sentiment analysis can more accurately reflect users’real interests and preferences,and then effectively improve the recommendation accuracy.