Yan Yongjie, Yu Guang, Yan Xiangbin
School of Management, Harbin Institute of Technology, Harbin 150001, China.
School of Mathematics and Computer Science, Jiangxi Science and Technology Normal University, Nanchang 330038, China.
Entropy (Basel). 2022 Apr 11;24(4):535. doi: 10.3390/e24040535.
Most of the existing recommendation systems using deep learning are based on the method of RNN (Recurrent Neural Network). However, due to some inherent defects of RNN, recommendation systems based on RNN are not only very time consuming but also unable to capture the long-range dependencies between user comments. Through the sentiment analysis of user comments, we can better capture the characteristics of user interest. Information entropy can reduce the adverse impact of noise words on the construction of user interests. Information entropy is used to analyze the user information content and filter out users with low information entropy to achieve the purpose of filtering noise data. A self-attention recommendation model based on entropy regularization is proposed to analyze the emotional polarity of the data set. Specifically, to model the mixed interactions from user comments, a multi-head self-attention network is introduced. The loss function of the model is used to realize the interpretability of recommendation systems. The experiment results show that our model outperforms the baseline methods in terms of MAP (Mean Average Precision) and NDCG (Normalized Discounted Cumulative Gain) on several datasets, and it achieves good interpretability.
大多数现有的深度学习推荐系统都是基于循环神经网络（RNN）的方法。然而，由于RNN存在一些固有缺陷，基于RNN的推荐系统不仅非常耗时，而且无法捕捉用户评论之间的长期依赖关系。通过对用户评论进行情感分析，我们可以更好地捕捉用户兴趣特征。信息熵可以减少噪声词对用户兴趣构建的不利影响。利用信息熵分析用户信息内容，过滤掉信息熵低的用户，以达到过滤噪声数据的目的。提出了一种基于熵正则化的自注意力推荐模型，用于分析数据集的情感极性。具体来说，为了对来自用户评论的混合交互进行建模，引入了多头自注意力网络。该模型的损失函数用于实现推荐系统的可解释性。实验结果表明，在多个数据集上，我们的模型在平均准确率（MAP）和归一化折损累计增益（NDCG）方面优于基线方法，并且具有良好的可解释性。
