图神经网络推荐方法研究

doi:10.3778/j.issn.1002-8331.2110-0345

摘要/Abstract

摘要： 图神经网络因其特性在许多应用领域展露锋芒，将图神经网络与推荐相结合成为研究热点之一。在推荐中使用图神经网络方法，能够在复杂环境中显著提高推荐的水平。对图神经网络的方法、个性化推荐和群组推荐分别进行总结介绍；对基于图神经网络的推荐方法进行概述，重点对图神经网络及其近年来在推荐领域的研究成果进行归纳总结；分析了推荐研究现状和阻碍其进一步发展的困难，并根据图神经网络的优势对图神经网络与群组推荐结合进行了可行性分析及展望。

关键词: 图神经网络, 群组推荐, 深度学习, 推荐系统

Abstract: Graph neural network has a good application effect in many application fields because of its characteristics, the combination of graph neural network and recommendation has become one of the research hot spots. Using graph neural network in recommendation can significantly improve the level of recommendation in complex environment. In this paper, the graph neural network method, personalized recommendation and group recommendation are introduced respectively. The recommendation methods based on graph neural network are summarized, focusing on graph neural network and its recent research achievements in the field of recommendation. The recommendation research status and the difficulties in further development are analyzed. According to the advantages of graph neural network, the feasibility of combining graph neural network with group recommendation is analyzed and prospected.

Key words: graph neural network, group recommendation, deep learning, recommendation system

刘鑫, 梅红岩, 王嘉豪, 李晓会. 图神经网络推荐方法研究[J]. 计算机工程与应用, 2022, 58(10): 41-49.

LIU Xin, MEI Hongyan, WANG Jiahao, LI Xiaohui. Research on Graph Neural Network Recommendation Method[J]. Computer Engineering and Applications, 2022, 58(10): 41-49.

参考文献

[1] 廖国琼，杨乐川，万常选，等.注意力感知的群组Next事件推荐策略[J/OL].计算机科学与探索[2021-10-07].http：//kns.cnki.net/kcms/detail/11.5602.TP.20210818.1807.006.html.
LIAO G Q，YANG L C，WAN C X，et al.Attention-aware next event recommendation strategy for group[J/OL].Journal of Frontiers of Computer Science and Technology[2021-10-07].http：//kns.cnki.net/kcms/detail/11.5602.TP.20210818.
1807.006.html.
[2] 王永贵，林佳敏，何佳玉.融合领导者影响与隐式信任度的群组推荐方法[J/OL].计算机工程与应用[2021-10-07].http：//kns.cnki.net/kcms/detail/11.2127.TP.20210913.1402.016.html.
WANG Y G，LIN J M，HE J Y.Group recommendation method integrating leader influence and implicit trust[J/OL].Computer Engineering and Applications[2021-10-07].http：//kns.cnki.net/kcms/detail/11.2127.TP.20210913.1402.016.html.
[3] 李方文.基于用户生成空间数据的群组旅游推存研究[D].武汉：华中师范大学，2019.
LI F W.Research on group tourism storage based on user generatedspatial data[D].Wuhan：Central China Normal University，2019.
[4] MEZEI Z，EICKHOFF C.Evaluating music recommender systems for groups[J].arXiv：1707.09790，2017.
[5] ZHANG C，ZHANG H，WANG J.Personalized restaurant recommendation method combining group correlations and customer preferences[J].Information Sciences，2018，454：128-143.
[6] 夏立新，杨金庆，程秀峰.移动环境下融合情境信息的群组推荐模型研究——基于用户APP行为数据的实证分析[J].情报学报，2018，37（4）：384-393.
XIA L X，YANG J Q，CHENG X F.Research on group recommendation model integrating contextual information in mobile environment—empirical analysis based on user APP behavior data[J].Journal of Information Science，2018，37（4）：384-393.
[7] 吴国栋，查志康，涂立静，等.图神经网络推荐研究进展[J].智能系统学报，2020，15（1）：14-24.
WU G D，ZHA Z K，TU L J，et al.Progress in graph neural network recommendation research[J].Journal of Intelligent Systems，2020，15（1）：14-24.
[8] WITHNALL M，LINDEL?F E，ENGKVIST O，et al.Building attention and edge message passing neural networks for bioactivity and physical chemical property prediction[J].Journal of Cheminformatics，2020，12（1）：1-18.
[9] VERDIER H，DUVAL M，LAURENT F，et al.Learning physical properties of anomalous random walks using graph neural networks[J].Journal of Physics A：Mathematical and Theoretical，2021，54（23）：234001.
[10] FOUT A M.Protein interface prediction using graph convolutional networks[D].Colorado State University，2017.
[11] ZHANG X M，LIANG L，LIU L，et al.Graph neural networks and their current applications in bioinformatics[J].Frontiers in Genetics，2021，12：690049.
[12] JIN S，ZENG X，XIA F，et al.Application of deep learning methods in biological networks[J].Briefings in Bioinformatics，2021，22（2）：1902-1917.
[13] WANG Z，LV Q，LAN X，et al.Cross-lingual knowledge graph alignment viagraph convolutional networks[C]//Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing，2018：349-357.
[14] KAMPFFMEYER M，CHEN Y，LIANG X，et al.Rethinking knowledge graph propagation for zero-shot learning[C]//Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition，2019：11487-11496.
[15] YU C，LIU Y，GAO C，et al.Representative graph neural network[C]//16th European Conference on Computer Vision.Cham：Springer，2020：379-396.
[16] GAO D，LI K，WANG R，et al.Multi-modal graph neural network for joint reasoning on vision and scene text[C]//Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition，2020：12746-12756.
[17] SHI Y，LI Q，ZHU X X.Building segmentation through a gated graph convolutional neural network with deep structured feature embedding[J].ISPRS Journal of Photogrammetry and Remote Sensing，2020，159：184-197.
[18] GAO J，QING L，LI L，et al.Multi-scale features based interpersonal relation recognition using higher-order graph neural network[J].Neurocomputing，2021，456：243-252.
[19] ZHANG Y，LIU Q，SONG L.Sentence-state LSTM for text representation[J].arXiv：1805.02474，2018.
[20] ZHANG Y，YU X，CUI Z，et al.Every document owns its structure：inductive text classification via graph neural networks[J].arXiv：2004.13826，2020.
[21] JING B，YOU Z，YANG T，et al.Multiplex graph neural network for extractive text summarization[J].arXiv：2108.
12870，2021.
[22] SHI C，XU M，ZHU Z，et al.GraphAF：a flow-based auto-
regressive model for molecular graph generation[J].arXiv：2001.09382，2020.
[23] LI Y，VINYALS O，DYER C，et al.Learning deep generative models of graphs[J].arXiv：1803.03324，2018.
[24] GAO C，ZHENG Y，LI N，et al.Graph neural networks for recommender systems：challenges，methods，and directions[J].arXiv：2109.12843，2021.
[25] ZHANG Y，QI P，MANNING C D.Graph convolution over pruned dependency trees improves relation extraction[J].arXiv：1809.10185，2018.
[26] YING R，HE R，CHEN K，et al.Graph convolutional neural networksfor web-scale recommender systems[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining，2018：974-983.
[27] 吴云昌，刘柏嵩，王洋洋，等.群组推荐分析与研究综述[J].电信科学，2018，34（12）：71-83.
WU Y C，LIU B S，WANG Y Y，et al.Group recommendation analysis and research review[J].Telecommunication Science，2018，34（12）：71-83.
[28] 张玉洁，杜雨露，孟祥武.组推荐系统及其应用研究[J].计算机学报，2016，39（4）：745-764.
ZHANG Y J，DU Y L，MENG X W.Group recommendation system and its application[J].Chinese Journal of Computers，2016，39（4）：745-764.
[29] BERG R，KIPF T N，WELLING M.Graph convolutional matrix completion[J].arXiv：1706.02263，2017.
[30] SONG W，XIAO Z，WANG Y，et al.Session-based social recommendation via dynamic graph attention networks[C]//Proceedings of the 20th ACM International Conference on Web Search and Data Mining，2019：555-563.
[31] CUI Z，LI Z，WU S，et al.Dressing as a whole：outfit compatibility learning based on node-wise graph neural networks[C]//The World Wide Web Conference，2019：307-317.
[32] WANG X，HE X，WANG M，et al.Neural graph collaborative filtering[C]//Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval，Paris，2019：165-174.
[33] FAN W，MA Y，LI Q，et al.Graph neural networks for social recommendation[C]//The World Wide Web Conference，2019：417-426.
[34] LI X，WANG X，HE X，et al.Hierarchical fashion graph network for personalized outfit recommendation[J].arXiv：2005.12566，2020.
[35] WANG H，ZHAO M，XIE X，et al.Knowledge graph convolutional networks for recommender systems[J].arXiv：1904.12575，2019.
[36] ZHENG L，LU C T，JIANG F，et al.Spectral collaborative filtering[C]//Proceedings of the 12th ACM Conference on Recommender Systems，2018：311-319.
[37] DEFFERRARD M，BRESSON X，VANDERGHEYNST P.Convolutional neural networks on graphs with fast localized spectral filtering[C]//Advances in Neural Information Processing Systems 29：Annual Conference on Neural Information Processing Systems，2016：3844-3852.
[38] KIPF T N，WELLING M.Semi-supervised classificationwith graph convolutional networks[J].arXiv：1609.02907，2016.
[39] LIU Z，ZHENG L，ZHANG J，et al.JSCN：joint spectral convolutional network for cross domain recommendation[C]//2019 IEEE International Conference on Big Data，2019：850-859.
[40] MAN T，SHEN H，JIN X，et al.Cross-domain recommendation：an embedding and mapping approach[C]//26th International Joint Conference on Artificial Intelligence，2017：2464-2470.
[41] CHANG H，RONG Y，XU T，et al.Spectral graph attention network[J].arXiv：2003.07450，2020.
[42] XU B，SHEN H，CAO Q，et al.Graph wavelet neural network[J].arXiv：1904.07785，2019.
[43] ZHU H，KONIUSZ P.Simple spectral graph convolution[C]//9th International Conference on Learning Representations，2021.
[44] 王海艳，肖亦康.基于密度峰值聚类的动态群组发现方法[J].计算机研究与发展，2018，55（2）：391-399.
WANG H Y，XIAO Y K.Dynamic group discovery method based on density peak clustering[J].Computer Research and Development，2018，55（2）：391-399.
[45] 李鹏，于晓洋，孙渤禹.基于用户群组行为分析的视频推荐方法研究[J].电子与信息学报，2014，36（6）：1485-1491.
LI P，YU X Y，SUN B Y.Research on video recommendation method based on user group behavior analysis[J].Journal of Electronics & Information Technology，2014，36（6）：1485-1491.
[46] 汪祥舜，郑孝遥，朱德义，等.基于偏好融合的群组推荐研究[J].南京信息工程大学学报（自然科学版），2019，11（5）：601-608.
WANG X S，ZHENG X Y，ZHU D Y，et al.Research on group recommendation based on preference fusion[J].Journal of Nanjing University of Information Science & Technology（Natural Science），2019，11（5）：601-608.
[47] 宣鹏程，唐彦，王汪送.基于项目类型的群组推荐方法[J].电子测量技术，2019，42（7）：54-58.
XUAN P C，TANG Y，WANG W S.Group recommendation based on project type[J].Electronic Measurement Technology，2019，42（7）：54-58.
[48] LI Y，TARLOW D，BROCKSCHMIDT M，et al.Gated graph sequence neural networks[J].arXiv：1511.05493，2015.
[49] 魏晓辉，孙冰怡，崔佳旭.基于图神经网络的兴趣活动推荐算法[J].吉林大学学报（工学版），2021，51（1）：278-284.
WEI X H，SUN B Y，CUI J X.Interest activity recommendation algorithm based on graph neural network[J].Journal of Jilin University（Engineering and Technology Edition），2021，51（1）：278-284.
[50] 王海艳，董茂伟.基于动态卷积概率矩阵分解的潜在群组推荐[J].计算机研究与发展，2017，54（8）：1853-1863.
WANG H Y，DONG M W.Latent group recommendation based on dynamic probabilistic matrix factorization model integrated with CNN[J].Journal of Computer Research and Development，2017，54（8）：1853-1863.
[51] LI Y，YU R，SHAHABI C，et al.Diffusion convolutional recurrent neural network：data-driven traffic forecasting[J].arXiv：1707.01926，2017.
[52] YU B，YIN H，ZHU Z.Spatio-temporal graph convolutional networks：a deep learning framework for traffic forecasting[J].arXiv：1709.04875，2017.
[53] ZHANG J，GAO C，JIN D，et al.Group-buying recommendation for social e-commerce[J].arXiv：2010.06848，2020.