«上一篇/Previous Article|本期目录/Table of Contents|下一篇/Next Article»

《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:

43

期数:

2022年02期

页码:

44-50

栏目:

出版日期:

2022-02-27

文章信息/Info

Title:

Research on Recommendation Method Based on Sentimental Attention of Review Text

作者:

郑建兴¹; 郭彤彤¹; 申利华²; 李德玉¹

1.山西大学计算机与信息技术学院；2.山西省信息产业技术研究院有限公司;

Author(s):

ZHENG Jianxing¹;  GUO Tongtong¹;  SHEN Lihua²;  LI Deyu¹

1.College of Computer and Information Technology, Shanxi University, Taiyuan 030006, China; 

2.Shanxi Information Industry Technology Research Institute Co., Ltd., Taiyuan 030012, China

关键词:

评论文本; 情感特征; 注意力机制; 推荐

Keywords:

review text;  sentimental features;  attention mechanism;  recommendation

分类号:

TP301

DOI:

10.13705/j.issn.1671-6833.2022.02.007

文献标志码:

A

摘要:

基于评论文本的深度学习推荐主要利用评论文本刻画用户和项目的特征信息，建模用户对项目的评分关系。已有方法在提高推荐系统可解释性的同时，忽略了情感特征在评分预测中的贡献。本文考虑了评论文本以及情感极性分别在用户和项目嵌入表示中的作用，提出了一种基于评论文本情感注意力的推荐方法（IncorRAS-Rec）。首先，通过CNN对用户和项目的评论文本进行评论特征表示，同时结合了用户对项目的评分偏好，建模用户和项目的评论情感特征表示；其次，基于注意力机制为用户和项目聚合了相关的评论情感特征信息，学习用户和项目的嵌入表示；最后，结合偏置信息，预测了用户对项目的评分。在亚马逊公开数据集上进行了实验，并验证方法的有效性。实验表明，IncorRAS-Rec在RMSE、MAE指标上的性能要优于其他传统方法。

Abstract:

Recommendation methods of deep learning-based on review text mainly means to describe the feature information of users and items by terms of review texts, by rating relationship between users and items to improve the recommendation performance. Existing studies ignore the interpretable contribution of sentimental features on the rating prediction. To solve this problem, by incorporating the roles of review text and sentimental polarity orientation in the embeddings of users and items, respectively, a sentimental attention recommendation method was proposed based on review text (IncorRAS-Rec); Firstly, CNN (convolutional neural network) was used to handle review sets for users and items, represent the review features of users and items, and obtain relevant users features and items features; Then, by combining users′ rating preference for items, users and items embedding with reviews′ sentimental features were learned. Secondly, by aggregating reviews′ relevant sentimental feature information for users and items in terms of attention mechanism, the embeddings of users and items were learned; Finally, the users ratings on the items were predicted based on users and items embeddings together with their bias information. The experimental comparison and analysis were carried out on public Amazon datasets, to evaluates the effectiveness of the model performance. Experimental results showed that the proposed IncorRAS-Rec model not only could outperform other traditional methods in terms of RMSE(Root mean square error) and MAE(Mean absolute error) metrics, but also implement the explanatory role of sentimental features in rating prediction.

常用功能

更新日期/Last Update: 2022-02-25