基于多通道CNN-BiGRU与多特征融合方法

王凯丽; 李海明

doi:10.19304/J.ISSN1000-7180.2021.0987

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基于多通道CNN-BiGRU与多特征融合方法

人工智能与算法 | 更新时间：2026-09-04

- 基于多通道CNN-BiGRU与多特征融合方法
- A method with multi-channel CNN-BiGRU and Multi-feature fusion
- 微电子学与计算机 2022年39卷第3期页码：41-49
- 作者机构：
  
  上海电力大学计算机科学与技术学院,上海,200000
- 作者简介：
- 基金信息：
  
  国家自然科学基金面上项目 61772327上海自然科学基金面上项目 20ZR1455900
- DOI：10.19304/J.ISSN1000-7180.2021.0987
  中图分类号：
- 纸质出版：2022
- 稿件说明：
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王凯丽, 李海明. 基于多通道CNN-BiGRU与多特征融合方法[J]. 微电子学与计算机, 2022, 39(3): 41-49.

WANG Kaili, LI Haiming. A method with multi-channel CNN-BiGRU and Multi-feature fusion[J]. 2022, 39(3): 41-49.
王凯丽, 李海明. 基于多通道CNN-BiGRU与多特征融合方法[J]. 微电子学与计算机, 2022, 39(3): 41-49. DOI： 10.19304/J.ISSN1000-7180.2021.0987.

WANG Kaili, LI Haiming. A method with multi-channel CNN-BiGRU and Multi-feature fusion[J]. 2022, 39(3): 41-49. DOI： 10.19304/J.ISSN1000-7180.2021.0987.

摘要

新闻推荐是根据用户的阅读习惯，为其推送更符合需求的内容，然而现有的方法仍存在特征学习不足的问题.针对此问题，提出了一种基于多通道CNN-BiGRU与多特征融合方法，主要由以下四部分组成：(1)词嵌入层.在词向量中融入实体嵌入向量，弥补单独仅使用词向量的不足，完成多通道词向量的构建；(2)多通道CNN-BiGRU模型.此部分模型使用卷积神经网络(CNN)提取语句的局部特征，使用双向门控循环单元(BiGRU)提取文本的长序列依赖关系，同时，借助最大池化操作以减少参数数量，避免冗余，借助注意力网络以关注重要词汇的特征，获取精确的特征表示；(3)多特征融合.借助注意力机制融合多个新闻特征，并关注重要特征的内容，完成新闻表征的构建；(4)用户表征提取.通过多头注意力机制提取用户历史浏览记录中新闻间的交互关系，以构建准确的用户表征，完成推荐.实验结果表明，所提出的CBMR模型相对现有的CNN、DKN、TANR和NRMS模型，在AUC、MRR、nDCG@5和nDCG@10指标上表现更优异.

Abstract

According to the reading habits of users

news recommendations can push more suitable content for them

but the existing methods still have the problem of insufficient feature learning. To solve this problem

a method based on multi-channel CNN-BiGRU and multi-feature fusion is proposed

which is mainly composed of the following four parts: (1) Word Embedding Layer. Entity embedding vectors are integrated into word vectors to make up for the deficiency of using word embedding vectors and construct multi-channel word vectors; (2) Multi-channel CNN-BiGRU Model. In the model

a convolutional neural network (CNN) is used to extract the local features of sentences

and a bidirectional gated recurrent unit (BiGRU) is used to extract the long sequence dependency of text. At the same time

the maximum pool algorithm is used to reduce the number of parameters and avoid redundancy

and the attention network is used to pay attention to the features of key words to extract accurate features; (3) Multi-feature Fusion. With the help of attention mechanism

integrate multiple news features and pay attention to important features to complete the construction of news representation; (4) User Encoder. The interaction of news in the browsing histories of a user is extracted by multi-headed self-attention mechanism to construct user representation and complete recommendation.The experimental results show that compared with CNN

DKN

TANR

and NRMS models

CBMR performs better in AUC

MRR

nDCG@5

and nDCG@10 indicators.

关键词

Keywords

references

PENNINGTON J, SOCHER R, MANNING C. GloVe: global vectors for word representation[C]//Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). Doha, Qatar: Association for Computational Linguistics, 2014: 1532-1543. DOI: 10.3115/v1/d14-1162.

OKURA S, TAGAMI Y, ONO S, et al. Embedding-based news recommendation for millions of users[C]//Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Halifax: ACM, 2017: 1933-1942. DOI: 10.1145/3097983.3098108.

WU C H, WU F Z, AN M X, et al. NPA: neural news recommendation with personalizedattention[C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery % Data Mining. Anchorage: ACM, 2019: 2576-2584. DOI: 10.1145/3292500.3330665.

王海艳, 胡阳. 基于会话的双层注意力机制新闻推荐方法[J]. 南京邮电大学学报(自然科学版), 2021, 41(1): 99-106. DOI: 10.14132/j.cnki.1673-5439.2021.01.012. WANG H Y, HU Y. News recommendation method for session-based neural neuralnetwork with two-layer attention mechanism[J]. Journal of Nanjing University of Posts and Telecommunications(Natural Science Edition), 2021, 41(1): 99-106. DOI: 10.14132/j.cnki.1673-5439.2021.01.012.

WU F Z, QIAO Y, CHEN J H, et al. MIND: a large-scale dataset for news recommendation[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 2020: 3597-3606. DOI: 10.18653/v1/2020.acl-main.331.

WANG H W, ZHANG F Z, XIE X, et al. DKN: deep knowledge-aware network for news recommendation[C]//Proceedings of 2018 World Wide Web Conference. Lyon, France: ACM, 2018: 1835-1844. DOI: 10.1145/3178876.3186175.

RENDLE S. Factorization machines[C]//Proceedings of 2010 IEEE International Conference on Data Mining. Sydney, NSW, Australia: IEEE, 2010: 995-1000. DOI: 10.1109/icdm.2010.127.

ZHU Q N, ZHOU X F, SONG Z L, et al. DAN: deep attention neural network for news recommendation[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2019, 33(1): 5973-5980. DOI: 10.1609/aaai.v33i01.3301 5973.

WU C H, WU F Z, GE S Y, et al. Neural news recommendation with multi-head self-attention[C]//Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Hong Kong, China: Association for Computational Linguistics, 2019: 6389-6394. DOI: 10.18653/v1/d19-1671.

GUO H F, TANG R M, YE Y M, et al. DeepFM: a factorization-machine based neural network for CTR prediction[C]//Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence. Melbourne, Australia: ACM, 2017. DOI: 10.24963/ijcai.2017/239.

RICHARDSON M, DOMINOWSKA E, RAGNO R. Predicting clicks: estimating the click-through rate for new ads[C]//Proceedings of the 16th International Conference on World Wide Web. Banff, Alberta: ACM, 2007: 521-530. DOI: 10.1145/1242572.1242643.

KINGMA D, BA J. Adam: a method for stochastic optimization[C]//3rd International Conference on Learning Representations. San Diego: ICLR, 2015.

吴方照, 武楚涵, 安鸣霄, 等. 基于深度学习的个性化新闻推荐[J]. 南京信息工程大学学报(自然科学版), 2019, 11(3): 278-285. DOI: 10.13878/j.cnki.jnuist.2019.03.005. WU F Z, WU C H, AN M X, et al. Personalized news recommendation based on deep learning[J]. Journal of Nanjing University of Information Sciencew and Technology(Natural Science Edition), 2019, 11(3): 278-285. DOI: 10.13878/j.cnki.jnuist.2019.03.005.

KIM Y. Convolutional neural networks for sentence classification[C]//Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). Doha, Qatar: Association for Computational Linguistics, 2014. DOI: 10.3115/v1/d14-1181.

VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[J]. arXiv preprint arXiv: 1706.03762, 2017.

CHENG H T, KOC L, HARMSEN J, et al. Wide % deep learning for recommender systems[C]//Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. Boston: ACNM, 2016: 7-10. DOI: 10.1145/2988450.2988454.

WU C H, WU F Z, AN M X, et al. Neural news recommendation with topic-aware news representation[C]//Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Florence, Italy: Association for Computational Linguistics, 2019: 1154-1159. DOI: 10.18653/v1/p19-1110.

YANG Z C, YANG D Y, DYER C, et al. Hierarchical attention networks for document classification[C]//Proceedings of the 2016 conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. San Diego, California: Association for Computational Linguistics, 2016: 1480-1489. DOI: 10.18653/v1/n16-1174.

AN M X, WU F Z, WU C H, et al. Neural news recommendation with long-and short-term user representations[C]//Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Florence, Italy: ACL, 2019: 336-345. DOI: 10.18653/v1/P19-1033.

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