融合评论文本特征和评分图卷积表示的推荐模型

冯海林; 张潇; 刘同存

doi:10.11959/j.issn.1000-436x.2022049

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融合评论文本特征和评分图卷积表示的推荐模型

学术论文 | 更新时间：2024-06-05

- 融合评论文本特征和评分图卷积表示的推荐模型
- Recommendation model combining review’s feature and rating graph convolutional representation
- 通信学报 2022年43卷第3期页码：164-171
- 作者机构：
  
  浙江农林大学数学与计算机科学学院，浙江杭州 311300
- 作者简介：
  
  [ "冯海林（1980- ），男，安徽安庆人，博士，浙江农林大学教授、博士生导师，主要研究方向为计算机视觉、智能信息处理和物联网" ]
  [ "张潇（1997- ），女，浙江台州人，浙江农林大学硕士生，主要研究方向为深度学习和推荐算法" ]
  [ "刘同存（1983- ），男，山东临沂人，博士，浙江农林大学讲师，主要研究方向为数据挖掘、视频理解、深度学习和推荐算法" ]
- 基金信息：
  
  国家自然科学基金资助项目(U1809208);浙江省自然科学基金资助项目(LGG22F020010)
- DOI：10.11959/j.issn.1000-436x.2022049
  中图分类号： TP391
- 网络出版日期：2022-03，
  
  纸质出版日期：2022-03-25
- 稿件说明：
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冯海林, 张潇, 刘同存. 融合评论文本特征和评分图卷积表示的推荐模型[J]. 通信学报, 2022,43(3):164-171.

Hailin FENG, Xiao ZHANG, Tongcun LIU. Recommendation model combining review’s feature and rating graph convolutional representation[J]. Journal on communications, 2022, 43(3): 164-171.
冯海林, 张潇, 刘同存. 融合评论文本特征和评分图卷积表示的推荐模型[J]. 通信学报, 2022,43(3):164-171. DOI： 10.11959/j.issn.1000-436x.2022049.

Hailin FENG, Xiao ZHANG, Tongcun LIU. Recommendation model combining review’s feature and rating graph convolutional representation[J]. Journal on communications, 2022, 43(3): 164-171. DOI： 10.11959/j.issn.1000-436x.2022049.

摘要

为了充分利用评分的有效信息，并进一步研究评论的重要性，提出了一种融合评论文本特征和评分图卷积表示的推荐模型，利用图卷积编码学习用户和商品在评分上的特征表示，结合文本卷积特征，使用注意力机制来区分评论的重要性，然后通过隐因子模型把在评论和评分上学习到的特征表示融合产生推荐。在亚马逊公开数据集上的实验结果表明，提出的模型显著优于现有的模型，证明了提出的模型的有效性。

Abstract

In order to fully exploit the effective information of the ratings and further investigate the importance of the review

a recommendation model combining review’s feature and rating graph convolutional representation was proposed.Graph convolutional neural network was used to learn the representation of user and item from the ratings data.Combining with text convolutional features

attention mechanism was utilized to distinguish the importance of the review.Finally

the representation learned from the review and the rating data was fused by the hidden factor model.The experimental results on Amazon’s public data showed that the proposed model significantly outperformed the traditional approaches

proving the effectiveness of the proposed model.

关键词

Keywords

references

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