融入情感分析的多层交互深度推荐模型研究<sup>*</sup>

doi:10.11925/infotech.2096-3467.2022.0228

数据分析与知识发现

2023, Vol. 7

Issue (3): 43-57 https://doi.org/10.11925/infotech.2096-3467.2022.0228

研究论文

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融入情感分析的多层交互深度推荐模型研究^*

李浩君(

),吕韵,汪旭辉,黄诘雅

浙江工业大学教育科学与技术学院杭州 310023

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

Li Haojun(

),Lv Yun,Wang Xuhui,Huang Jieya

College of Education Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China

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摘要

【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。

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	李浩君
	吕韵
	汪旭辉
	黄诘雅

关键词 ：情感分析, 特征挖掘, 神经网络, 多层交互, 推荐模型

Abstract：

[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.

Key words： Sentiment Analysis Feature Mining Neural Networks Multi-Layer Interaction Recommendation Models

收稿日期: 2022-03-18 出版日期: 2023-04-13

ZTFLH:

TP391

基金资助:国家自然科学基金面上项目(62077043);浙江省哲学社会科学规划交叉学科重点支持课题(22JCXK05Z)

通讯作者: 李浩君，ORCID:0000-0003-0318-6528，E-mail:zgdlhj@zjut.edu.cn。

引用本文:

李浩君, 吕韵, 汪旭辉, 黄诘雅. 融入情感分析的多层交互深度推荐模型研究^*[J]. 数据分析与知识发现, 2023, 7(3): 43-57.
Li Haojun, Lv Yun, Wang Xuhui, Huang Jieya. A Deep Recommendation Model with Multi-Layer Interaction and Sentiment Analysis. Data Analysis and Knowledge Discovery, 2023, 7(3): 43-57.

链接本文:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2022.0228 或 https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2023/V7/I3/43

符号	定义
$r u, i$	用户 $u$ 对资源 $i$ 的真实评分
$r^u, i$	用户 $u$ 对资源 $i$ 的预测评分
$R e u, i$	用户 $u$ 对资源 $i$ 的评论
$U L F M$	用户浅层特征
$I L F M$	资源浅层特征
$U T E X T$	用户深层特征
$I T E X T$	资源深层特征
$a u, m$	用户 $u$ 第 $m$ 条评论对应的贡献度权值
$a i, n$	资源 $i$ 第 $n$ 条评论对应的贡献度权值

Table 1 符号定义

Fig.1 DeepSAMI模型整体框架

Fig.2 基于情感分析的评论文本浅层特征挖掘结构

Fig.3 结合CNN与注意力机制的评论文本深层特征挖掘结构图

Table 2 数据集基本信息

Table 3 基线模型对比

Table 4 情感分析实验结果

Table 5 部分评分数据更新前后对比

Table 6 各模型在4个数据集上的MAE对比

Table7 各模型在4个数据集上的RMSE对比

Fig.4 MLP隐藏层深度对模型性能影响

Fig.5 平衡因子 $α$ 对模型性能影响

Table 8 变体模型对比实验结果

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