Jilin University, Changchun, Jilin, China.
Jilin University, Changchun, Jilin, China.
Neural Netw. 2019 Mar;111:77-88. doi: 10.1016/j.neunet.2018.12.011. Epub 2019 Jan 8.
Traditional recommender systems rely on user profiling based on either user ratings or reviews through bi-sentimental analysis. However, in real-world scenarios, there are two common phenomena: (1) users only provide ratings for items but without detailed review comments. As a result, the historical transaction data available for recommender systems are usually unbalanced and sparse; (2) in many cases, users' opinions can be better grasped in their reviews than ratings. For the reason that there is always a bias between ratings and reviews, it is really important that users' ratings and reviews should be mutually reinforced to grasp the users' true opinions. To this end, in this paper, we develop an opinion mining model based on convolutional neural networks for enhancing recommendation. Specifically, we exploit two-step training neural networks, which utilize both reviews and ratings to grasp users' true opinions in unbalanced data. Moreover, we propose a Sentiment Classification scoring (SC) method, which employs dual attention vectors to predict the users' sentiment scores of their reviews rather than using bi-sentiment analysis. Next, a combination function is designed to use the results of SC and user-item rating matrix to catch the opinion bias. It can filter the reviews and users, and build an enhanced user-item matrix. Finally, a Multilayer perceptron based Matrix Factorization (MMF) method is proposed to make recommendations with the enhanced user-item matrix. Extensive experiments on several real-world datasets (Yelp, Amazon, Taobao and Jingdong) demonstrate that (1) our approach can achieve a superior performance over state-of-the-art baselines; (2) our approach is able to tackle unbalanced data and achieve stable performances.
传统的推荐系统依赖于基于用户评分或通过双情感分析的评论的用户档案。然而，在实际场景中，存在两种常见现象：（1）用户只为项目提供评分，而没有详细的评论。因此，推荐系统可用的历史交易数据通常是不平衡和稀疏的；（2）在许多情况下，用户的意见可以在评论中比评分更好地掌握。由于评分和评论之间总是存在偏差，因此用户的评分和评论应该相互加强以掌握用户的真实意见非常重要。为此，在本文中，我们开发了一种基于卷积神经网络的意见挖掘模型，用于增强推荐。具体来说，我们利用两步训练神经网络，利用评论和评分来掌握不平衡数据中用户的真实意见。此外，我们提出了一种情感分类评分（SC）方法，该方法采用双注意力向量来预测用户对评论的情感评分，而不是使用双情感分析。接下来，设计了一个组合函数，用于使用 SC 和用户-项目评分矩阵的结果来捕捉意见偏差。它可以过滤评论和用户，并构建增强的用户-项目矩阵。最后，提出了一种基于多层感知机的矩阵分解（MMF）方法，使用增强的用户-项目矩阵进行推荐。在几个真实数据集（Yelp、Amazon、Taobao 和 Jingdong）上进行的广泛实验表明：（1）我们的方法可以优于最先进的基线方法；（2）我们的方法能够解决不平衡数据并实现稳定的性能。
