计算机工程与科学 ›› 2022, Vol. 44 ›› Issue (02): 335-344.
李方,吴国栋,涂立静,刘玉良,查志康,李景霞
收稿日期:
2020-08-26
修回日期:
2020-10-31
接受日期:
2022-02-25
出版日期:
2022-02-25
发布日期:
2022-02-18
基金资助:
LI Fang,WU Guo-dong,TU Li-jing,LIU Yu-liang,ZHA Zhi-kang,LI Jing-xia
Received:
2020-08-26
Revised:
2020-10-31
Accepted:
2022-02-25
Online:
2022-02-25
Published:
2022-02-18
摘要: 图自编码器GAE是一种源自图神经网络的学习框架,在编码器中引入聚合邻域节点的思想,解码器对图结构数据进行解码,重构图结构数据;在模型中引入监督模块,可以提高图结构数据在模型中的嵌入完整性和数据生成的准确性;编解码可以采用不同的神经网络,从而利用不同神经网络的优点。近年来GAE推荐逐渐成为推荐系统研究的热点。从无监督学习与半监督学习方面分析了已有GAE推荐研究取得的进展;探讨了已有GAE推荐模型存在用户冷启动问题、可解释性差、模型复杂度高和难以处理数据的多源异构性等方面的问题;并从跨领域推荐,结合传统推荐方法,引入注意力机制,融合各类场景等研究方向对未来GAE推荐进行展望。
李方, 吴国栋, 涂立静, 刘玉良, 查志康, 李景霞. 图自编码器推荐研究综述[J]. 计算机工程与科学, 2022, 44(02): 335-344.
LI Fang, WU Guo-dong, TU Li-jing, LIU Yu-liang, ZHA Zhi-kang, LI Jing-xia. A review of graph auto-encoder recommendation[J]. Computer Engineering & Science, 2022, 44(02): 335-344.
[1] | Mooney R J, Roy L. Content-based book recommending using learning for text categorization[C]∥Proc of the 15th ACM Conference on Digital Libraries,2000:195-204. |
[2] | Sanz-Cruzado J,Castells P,Macdonald C,et al. Effective contact recommendation in social networks by adaptation of information retrieval models[J]. Information Processing & Management,2020,57(5):102285. |
[3] | Balabanovic M, Shoham Y. Fab:Content-based,collabo- rative recommendation[J]. Communications of the ACM,1997,40(3):66-72. |
[4] | Bai Bo,Liu Yu-ting,Ma Chi-cheng,et al. Graph neural network[J]. Scientia Sinica(Mathematica),2020,50(3):367-384.(in Chinese) |
[5] | Kusner M J,Paige B,Hernández-Lobato J M. Grammar variational autoencoder[C]// Proc of the 34th International Conference on Machine Learning,2017:1945-1954. |
[6] | Dai H J,Tian Y T,Dai B,et al. Syntax-directed variational autoencoder for molecule generation[C]∥Proc of the 31st Conference on Neural Information Processing Systems,2017:1-10. |
[7] | Wu Z,Pan S,Chen F,et al. A comprehensive survey on graph neural networks[J]. IEEE Transactions on Neural Networks and Learning Systems,2020,32(1):4-24. |
[8] | Rumelhart D E,Hinton G E,Williams R J. Learning representations by back-propagating errors[J]. Nature,1986,323(6088):533-536. |
[9] | Zhang Dong-fang,Chen Hai-yan,Wang Jian-dong. Survey of semi-supervised feature selection methods[J]. Application Research of Computers,2021,38(2):321-329.(in Chinese) |
[10] | Tschannen M, Bachem O, Lucic M. Recent advances in autoencoder-based representation learning[J]. arXiv:1812.05069,2018. |
[11] | Kipf T N,Welling M. Variational graph auto-encoders[J]. arXiv:1611.07308,2016. |
[12] | Pedregosa F,Varoquaux G,Gramfort A,et al. Scikit-learn:Machine learning in Python[J]. The Journal of Machine Learning Research,2011,12:2825-2830. |
[13] | Grover A,Leskovec J. Node2vec:Scalable feature learning for networks[C]∥Proc of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,2016:855-864. |
[14] | Berg R,Kipf T N,Welling M. Graph convolutional matrix completion[J]. arXiv:1706.02263,2017. |
[15] | Salha G, Hennequin R,Vazirgiannis M. Keep it simple:Graph autoencoders without graph convolutional networks[J]. arXiv:1910.00942,2019. |
[16] | Tran P V. Learning to make predictions on graphs with autoencoders[C]∥Proc of the 5th IEEE International Confe- rence on Data Science and Advanced Analytics,2018:237-245. |
[17] | Fang Yi-qiu,Yu Chen-xi,Ge Jun-wei. Top-N recommendation algorithm based on multiple denoising auto-encoder [J]. Application Research of Computers,2020,37(12):3582-3585.(in Chinese) |
[18] | Li I, Fabbri A, Hingmire S, et al. R-VGAE:Relational- variational graph autoencoder for unsupervised prerequisite chain learning[J]. arXiv:2004.10610,2020. |
[19] | Salha G,Limnios S,Hennequin R,et al. Gravity-inspired graph autoencoders for directed link prediction[C]∥Proc of the 28th ACM International Conference on Information and Knowledge Management,2019:589-598. |
[20] | Xu Q,Shen F,Liu L,et al. GraphCAR:Content-aware multimedia recommendation with graph autoencoder[C]∥Proc of the 41st International ACM SIGIR Conference on Research & Development in Information Retrieval,2018:981-984. |
[21] | Zheng Cheng,Wang Jian. Collaborative filtering recommendation for joint attention and autoencoder [J]. Computer Engineering and Applications,2021,57(10):139-145.(in Chinese) |
[22] | Kherad M, Bidgoly A J. Recommendation system using a deep learning and graph analysis approach[J]. arXiv:2004.08100,2020. |
[23] | Mahdavi S,Khoshraftar S,An A. Dynamic joint variational graph autoencoders[J]. arXiv:1910.01963,2019. |
[24] | Pan S,Hu R,Long G,et al. Adversarially regularized graph autoencoder for graph embedding[C]∥Proc of the 27th International Joint Conference on Artificial Intelligence(IJCAI-18),2018:2609-2615. |
[25] | Perozzi B,Al-Rfou R,Skiena S. DeepWalk:Online learning of social representations[C]∥Proc of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,2014:701-710. |
[26] | Wang D,Cui P,Zhu W. Structural deep network embedding[C]∥Proc of the 22nd ACM SIGKDD International Confe- rence on Knowledge Discovery and Data Mining,2016:1225-1234. |
[27] | Tang J,Qu M,Wang M Z,et al. LINE:Large-scale information network embedding[C]∥Proc of the 24th International Conference on World Wide Web,2015:1067-1077. |
[28] | Tang L,Liu H. Relational learning via latent social dimensions[C]∥Proc of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,2009:817-826. |
[29] | Liben-Nowell D, Kleinberg J. The link-prediction problem for social networks[J]. Journal of the American Society for Information Science and Technology,2007,58(7):1019-1031. |
[30] | Belkin M,Niyogi P. Laplacian eigenmaps for dimensionality reduction and data representation[J]. Neural Computation,2003,15(6):1373-1396. |
[31] | Yu W C,Zheng C,Cheng W,et al. Learning deep network representations with adversarially regularized autoencoders[C]∥Proc of the 24th ACM SIGKDD International Confe- rence on Knowledge Discovery and Data Mining,2018:2663-2671. |
[32] | Dai Q Y,Li Q,Tang J,et al. Adversarial network embedding[J].arXiv:1711.07838,2017. |
[33] | Ma C,Kang P,Wu B,et al. Gated attentive-autoencoder for content-aware recommendation[C]∥Proc of the 12th ACM International Conference on Web Search and Data Mining,2019:519-527. |
[34] | Yu J L,Yin H Z,Li J D,et al. Enhance social recommendation with adversarial graph convolutional networks[J]. IEEE Transactions on Knowledge and Data Engineering,2020:doi:10.1109/TKDE.2020.3033673. |
[35] | Yang C,Zhang J,Wang H,et al. Relation learning on social networks with multi-modal graph edge variational autoencoders[C]∥Proc of the 13th International Conference on Web Search and Data Mining,2020:699-707. |
[36] | Singh A P,Gordon G J. Relational learning via collective matrix factorization[C]∥Proc of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,2008:650-658. |
[37] | Li B,Yang Q,Xue X Y. Can movies and books collaborate? Cross-domain collaborative filtering for sparsity reduction[C]∥Proc of the 21st International Joint Conference on Artificial Intelligence,2009:2052-2057. |
[38] | Hu L,Cao J,Xu G,et al. Personalized recommendation via cross-domain triadic factorization[C]∥Proc of the 22nd International Conference on World Wide Web,2013:595-606. |
[39] | Hsieh C K,Yang L,Cui Y,et al. Collaborative metric learning[C]∥Proc of the 26th International Conference on World Wide Web,2017:193-201. |
[40] | Huang Li-wei, Jiang Bi-tao,Lü Shou-ye,et al. Survey on deep learning based recommender systems [J]. Chinese Journal of Computers,2018,41(7):1619-1647.(in Chinese) |
附中文参考文献: | |
[4] | 白铂,刘玉婷,马驰骋,等.图神经网络[J].中国科学:数学,2020,50(3):367-384. |
[9] | 张东方,陈海燕,王建东.半监督特征选择综述[J].计算机应用研究,2021,38(2):321-329. |
[17] | 方义秋,俞晨曦,葛君伟.基于多重降噪自编码器模型的top-N推荐算法[J].计算机应用研究,2020,37(12):3582-3585. |
[21] | 郑诚,王建.联合注意力和自编码器的协同过滤推荐[J].计算机工程与应用,2021,57(10):139-145. |
[40] | 黄立威,江碧涛,吕守业,等.基于深度学习的推荐系统研究综述[J].计算机学报,2018,41(7):1619-1647. |
[1] | 印杰, 黄肖宇, 刘家银, 牛博威, 谢文伟, . 基于预训练语言模型的安卓恶意软件检测方法[J]. 计算机工程与科学, 2023, 45(08): 1433-1442. |
[2] | 高玮蔚, 刘杨, 马慧芳, 唐月晨. 基于增强偏好影响力的图注意力网络推荐算法[J]. 计算机工程与科学, 2023, 45(07): 1300-1307. |
[3] | 王玫申, 张鹏, 薛乐洋, . 基于二分网络的长期推荐[J]. 计算机工程与科学, 2023, 45(04): 691-700. |
[4] | 罗可劲, 刘广聪, 杨文浩. 基于多任务学习的图神经网络推荐模型研究[J]. 计算机工程与科学, 2023, 45(04): 726-733. |
[5] | 张若一, 金柳, 马慧芳, 王亦可, 李清风. 融合相似用户影响效应的知识图谱推荐模型[J]. 计算机工程与科学, 2023, 45(03): 520-527. |
[6] | 邹程辉, 李卫疆, . 融合知识图谱和评论文本的个性化推荐模型[J]. 计算机工程与科学, 2023, 45(01): 181-190. |
[7] | 高永强, 张之明, 王宇涛. 基于Cross-DeepFM的军事训练推荐模型[J]. 计算机工程与科学, 2022, 44(08): 1364-1371. |
[8] | 王栋, 杨珂, 玄佳兴, 韩雨桐, 赵丽花, 王旭仁. 基于半监督生成对抗网络的恶意代码家族分类实现[J]. 计算机工程与科学, 2022, 44(05): 826-833. |
[9] | 贾俊杰, 姚叶旺, 陈旺虎. 基于非负矩阵分解的群组推荐算法[J]. 计算机工程与科学, 2022, 44(05): 933-943. |
[10] | 汪静, 钱晓东. 区块链环境中基于局部敏感哈希的协同过滤推荐研究[J]. 计算机工程与科学, 2022, 44(03): 436-446. |
[11] | 贾俊杰, 段超强. 基于评分离散度的托攻击检测算法[J]. 计算机工程与科学, 2022, 44(03): 554-562. |
[12] | 王保成, 刘利军, 黄青松, . 基于LDA和卷积神经网络的半监督图像标注方法[J]. 计算机工程与科学, 2022, 44(01): 110-117. |
[13] | 孙庞博, 符琦, 陈安华, 蒋云霞. 基于组合预测模型的小样本轴承故障分类诊断 [J]. 计算机工程与科学, 2021, 43(09): 1684-1691. |
[14] | 马汉达, 景迪. 基于SSD和时序模型的微博好友推荐算法[J]. 计算机工程与科学, 2021, 43(07): 1291-1298. |
[15] | 方梦华, 姜添, . 基于无监督学习的无人机目标跟踪[J]. 计算机工程与科学, 2021, 43(06): 1024-1031. |
阅读次数 | ||||||
全文 |
|
|||||
摘要 |
|
|||||
湘公网安备 43010502000083号
湘ICP备10006030号
版权所有 © 《计算机工程与科学》 编辑部
地址:中国湖南省长沙市开福区德雅路109号(410073) 电话:0731-87002567 Email: jsjgcykx@vip.163.com
本系统由北京玛格泰克科技发展有限公司设计开发 技术支持:support@magtech.com.cn