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-
网络首发：2022-03，
纸质出版：2022-03-25
- 稿件说明：
移动端阅览
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
SALAKHUTDINOV R R , MNIH A . Probabilistic matrix factorization [C ] // Proceedings of the 20th International Conference on Neural Information Processing Systems . New York:Curran Associates Inc , 2007 : 1257 - 1264 .
KOREN Y , BELL R , VOLINSKY C . Matrix factorization techniques for recommender systems [J ] . Computer , 2009 , 42 ( 8 ): 30 - 37 .
任开旭 , 王玉龙 , 刘同存 , 等 . 融合多维语义表示的概率矩阵分解模型 [J ] . 电子学报 , 2019 , 47 ( 9 ): 1848 - 1854 .
REN K X , WANG Y L , LIU T C , et al . A probabilistic matrix factorization model based on multidimensional semantic representation learning [J ] . Acta Electronica Sinica , 2019 , 47 ( 9 ): 1848 - 1854 .
KIM D , PARK C , OH J , et al . Convolutional matrix factorization for document context-aware recommendation [C ] // Proceedings of the 10th ACM Conference on Recommender Systems . New York:ACM Press , 2016 : 233 - 240 .
ZHENG L , NOROOZI V , YU P S . Joint deep modeling of users and items using reviews for recommendation [C ] // Proceedings of the Tenth ACM International Conference on Web Search and Data Mining . New York:ACM Press , 2017 : 425 - 434 .
LIU T C , LIAO J X , WANG Y L , et al . Collaborative tensor-topic factorization model for personalized activity recommendation [J ] . Multimedia Tools and Applications , 2019 , 78 ( 12 ): 16923 - 16943 .
顾亦然 , 姚朱鹏 , 杨海根 . 融合注意力胶囊的深度因子分解机模型 [J ] . 通信学报 , 2021 , 42 ( 10 ): 130 - 139 .
GU Y R , YAO Z P , YANG H G . Deep factorization machine model based on attention capsule [J ] . Journal on Communications , 2021 , 42 ( 10 ): 130 - 139 .
CHEN C , ZHANG M , LIU Y Q , et al . Neural attentional rating regression with review-level explanations [C ] // Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW’18 . New York:ACM Press , 2018 : 1583 - 1592 .
LIAO J X , LIU T C , YIN H Z , et al . An integrated model based on deep multimodal and rank learning for point-of-interest recommendation [J ] . World Wide Web , 2021 , 24 ( 2 ): 631 - 655 .
MCAULEY J , LESKOVEC J . Hidden factors and hidden topics:understanding rating dimensions with review text [C ] // Proceedings of the 7th ACM Conference on Recommender Systems . New York:ACM Press , 2013 : 165 - 172 .
BAO Y , FANG H , ZHANG J . TopicMF:simultaneously exploiting ratings and reviews for recommendation [C ] // Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence . Palo Alto:AAAI Press , 2014 : 2 - 8 .
WANG H , WANG N Y , YEUNG D Y . Collaborative deep learning for recommender systems [C ] // Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . New York:ACM Press , 2015 : 1235 - 1244 .
VINCENT P , LAROCHELLE H , LAJOIE I , et al . Stacked denoising autoencoders:learning useful representations in a deep network with a local denoising criterion [J ] . Journal of Machine Learning Research , 2010 , 11 : 3371 - 3408 .
LU Y C , DONG R H , SMYTH B . Coevolutionary recommendation model:mutual learning between ratings and reviews [C ] // Proceedings of the 2018 World Wide Web Conference on World Wide Web WWW’18 . New York:ACM Press , 2018 : 773 - 782 .
SEO S , HUANG J , YANG H , et al . Interpretable convolutional neural networks with dual local and global attention for review rating prediction [C ] // Proceedings of the Eleventh ACM Conference on Recommender Systems . New York:ACM Press , 2017 : 297 - 305 .
TAY Y , LUU A T , HUI S C . Multi-pointer co-attention networks for recommendation [C ] // Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery ＆ Data Mining . New York:ACM Press , 2018 : 2309 - 2318 .
MA C , KANG P , WU B , et al . Gated attentive-autoencoder for content-aware recommendation [C ] // Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining . New York:ACM Press , 2019 : 519 - 527 .
ZHOU J P , CHENG Z Y , PEREZ F , et al . TAFA:two-headed attention fused autoencoder for context-aware recommendations [C ] // Proceedings of the Fourteenth ACM Conference on Recommender Systems . New York:ACM Press , 2020 : 338 - 347 .
LIU H T , WU F Z , WANG W J , et al . NRPA:neural recommendation with personalized attention [C ] // Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval . New York:ACM Press , 2019 : 1233 - 1236 .
梁顺攀 , 刘伟 , 尤殿龙 , 等 . 考虑评论质量的自注意力胶囊网络评分预测模型 [J ] . 电子与信息学报 , 2021 , 43 ( 12 ): 3451 - 3458 .
LIANG S P , LIU W , YOU D L , et al . Self-attention capsule network rate prediction with review quality [J ] . Journal of Electronics ＆ Information Technology , 2021 , 43 ( 12 ): 3451 - 3458 .
冯兴杰 , 曾云泽 . 基于评分矩阵与评论文本的深度推荐模型 [J ] . 计算机学报 , 2020 , 43 ( 5 ): 884 - 900 .
FENG X J , ZENG Y Z . Joint deep modeling of rating matrix and reviews for recommendation [J ] . Chinese Journal of Computers , 2020 , 43 ( 5 ): 884 - 900 .
李昆仑 , 翟利娜 , 赵佳耀 , 等 . 融合信任关系与评论文本的矩阵分解推荐算法 [J ] . 小型微型计算机系统 , 2021 , 42 ( 2 ): 285 - 290 .
LI K L , ZHAI L N , ZHAO J Y , et al . Matrix factorization recommendation algorithms by exploiting trust relationship and review text [J ] . Journal of Chinese Computer Systems , 2021 , 42 ( 2 ): 285 - 290 .
GAO J Y , WANG X T , WANG Y S , et al . Explainable recommendation through attentive multi-view learning [C ] // Proceedings of the AAAI Conference on Artificial Intelligence . Palo Alto:AAAI Press , 2019 : 3622 - 3629 .
BERG R V D , KIPF T N , WELLING M . Graph convolutional matrix completion [J ] . arXiv Preprint,arXiv:1706.02263 , 2017 .
GAO J Y , LIN Y , WANG Y S , et al . Set-sequence-graph:a multi-view approach towards exploiting reviews for recommendation [C ] // Proceedings of the 29th ACM International Conference on Information ＆ Knowledge Management . New York:ACM Press , 2020 : 395 - 404 .
0
浏览量
1422
下载量
0
CSCD
- 网络PDF下载
- XML下载
- Meta-XML下载
- BibTeX下载
- Endnote下载
- RefMan 下载
- NoteExpress下载
- NoteFirst下载
关联资源
相关文章
相关作者
相关机构
AI问答- 技术支持由北京北大方正电子有限公司提供 京ICP备09064830号-19
京公网安备11010802024621 - 本系统建议在Chrome、 IE9+ 以上版本浏览器阅读本站内容，360浏览器请切换至极速模式
- Cookies帮助我们提供服务并提供个性化体验。使用本网站，即表示您同意我们使用Cookies
- 总访问量：584097 今日访问量：1913