2.765

2022影响因子

(CJCR)

  • 中文核心
  • EI
  • 中国科技核心
  • Scopus
  • CSCD
  • 英国科学文摘

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

融合社交因素和评论文本卷积网络模型的汽车推荐研究

冯永 陈以刚 强保华

冯永, 陈以刚, 强保华. 融合社交因素和评论文本卷积网络模型的汽车推荐研究. 自动化学报, 2019, 45(3): 518-529. doi: 10.16383/j.aas.2018.c170245
引用本文: 冯永, 陈以刚, 强保华. 融合社交因素和评论文本卷积网络模型的汽车推荐研究. 自动化学报, 2019, 45(3): 518-529. doi: 10.16383/j.aas.2018.c170245
FENG Yong, CHEN Yi-Gang, QIANG Bao-Hua. Social and Comment Text CNN Model Based Automobile Recommendation. ACTA AUTOMATICA SINICA, 2019, 45(3): 518-529. doi: 10.16383/j.aas.2018.c170245
Citation: FENG Yong, CHEN Yi-Gang, QIANG Bao-Hua. Social and Comment Text CNN Model Based Automobile Recommendation. ACTA AUTOMATICA SINICA, 2019, 45(3): 518-529. doi: 10.16383/j.aas.2018.c170245

融合社交因素和评论文本卷积网络模型的汽车推荐研究

doi: 10.16383/j.aas.2018.c170245
基金项目: 

重庆市重点产业共性关键技术创新专项 cstc2017zdcyzdyxx0047

国家重点研究发展计划 2017YFB1402400

广西可信软件重点实验室开放课题 kx201701

重庆市社会事业与民生保障科技创新专项 cstc2017shmsA20013

广西云计算与大数据协同创新中心开放课题 YD16E01

重庆市基础与前沿研究计划 cstc2017jcyjAX0340

国家自然科学基金 61762025

详细信息
    作者简介:

    陈以刚  重庆大学计算机学院硕士研究生.主要研究方向为智能推荐与神经网络.E-mail:20141413081@cqu.edu.cn

    强保华  桂林电子科技大学广西云计算与大数据协同创新中心教授.主要研究方向为大数据处理与信息检索.E-mail:qiangbh@guet.edu.cn

    通讯作者:

    冯永  重庆大学计算机学院教授.主要研究方向为大数据分析与数据挖据, 大数据管理与智能推荐, 大数据集成与人工智能.本文通信作者.E-mail:fengyong@cqu.edu.cn

Social and Comment Text CNN Model Based Automobile Recommendation

Funds: 

Key Industries Common Key Technologies Innovation Projects of Chongqing CSTC cstc2017zdcyzdyxx0047

National Key Research and development Program of China 2017YFB1402400

Guangxi Key Laboratory of Trusted Software kx201701

Social Undertakings and Livelihood Security Science and Technology Innovation Funds of Chongqing CSTC cstc2017shmsA20013

Guangxi Cooperative Innovation Center of Cloud Computing and Big Data YD16E01

Frontier and Application Foundation Research Program of Chongqing CSTC cstc2017jcyjAX0340

Supported by National Natural Science Foundation of China 61762025

More Information
    Author Bio:

    Master student at the College of Computer Science, Chongqing University. His research interest covers intelligent recommendation and neural networks

    Professor at the Guangxi Cooperative Innovation Center of Cloud Computing and Big Data, Guilin University of Electronic Technology. His research interest covers big data processing and information retrieval

    Corresponding author: FENG Yong Professor at the College of Computer Science, Chongqing University. His research interest covers big data analysis and data mining, big data management and intelligent recommendation and big data integration and artificial intelligence. Corresponding author of this paper
  • 摘要: 汽车作为较高价值和个性化的消费品,使得用户购车决策过程较一般商品更为复杂.本文主要研究社交环境和评论文本两方面对用户购车决策过程的影响,提出了融合社交因素和评论文本卷积网络的汽车推荐模型(Social and comment text CNN model based automobile recommendation,SCTCMAR).SCTCMAR首先定义了基于购买用途需求的社交圈,在此基础上提出了个人偏好计算方法,并引入了偏好相似度;其次,设计了卷积网络模型学习汽车评论文本的隐特征;然后将社交影响量化因素和评论文本特征有机融合注入推荐模型,并采用低阶矩阵分解技术进行模型计算.另外,本文使用GloVe预训练词嵌入模型,产生了SCTCMAR的另一个版本SCTCMAR+.最后,将SCTCMAR、SCTCMAR、FMM(Flexible mixture model)、TR(Trust rank)、Random sampling在课题组爬取后经清理、去重和整合的266995个用户、702辆汽车信息的真实数据集上进行精确率、召回率和平均倒序排名三个指标的多粒度实验比较,结果表明本文提出的SCTCMAR+和SCTCMAR具有良好的推荐性能.
    1)  本文责任编委 周涛
  • 图  1  SCTCMAR的图模型

    Fig.  1  Graphical model of SCTCMAR

    图  2  构造购买用途需求的社交圈

    Fig.  2  The inference process of purpose-based social circle of users

    图  3  卷积神经网络结构

    Fig.  3  Structure of convolutional neural network

    图  4  在精确率和召回率上本文提出的算法与其他算法的对比

    Fig.  4  Precision and recall comparisons between our algorithm and other algorithms

    图  5  各算法在平均倒序排名上的对比

    Fig.  5  ARHR comparison between our algorithm and other algorithms

    图  6  本文算法多因素联合对平均倒序排名的影响

    Fig.  6  ARHR line chart of impact of factors combination in our model

    表  1  推荐框架的符号定义

    Table  1  Notations in recommender framework

    表示符号概念描述
    $U=\left\{ {{u}_{1}}, \cdots, {{u}_{N}} \right\}$用户集合
    $F={{[{{F}_{u, v}}]}^{M\times M}}$偏好相似度矩阵
    $V=\{{{i}_{1}}, \cdots, {{i}_{M}}\}$汽车集合
    ${{F}_{u, v}}\in [0, 1]$用户$ u$和用户$ v$之间的偏好相似度
    $R={{[{{R}_{u, i}}]}^{N\times M}}$评分矩阵
    $Q={{[{{Q}_{u, i}}]}^{N\times M}}$用户个人偏好
    ${{R}_{u, i}}$用户$ u$对汽车$ i$的评分
    ${{Q}_{u, i}}\in [0, 1]$用户$ u$的偏好和汽车$ i$的主题之间的相关性
    $W$卷积神经网络(CNN)中的内部权重
    下载: 导出CSV

    表  2  数据集信息列表

    Table  2  The list of datasets

    名称来源/网址用途
    汽车之家http://www.autohome.com.cn 汽车及用户的相关数据信息
    网易汽车 http://auto.163.com 汽车及用户的相关数据信息
    搜狗实验室 http://www.sogou.com/labs/语料数据集
    汽车之家 http://www.autohome.com.cn语料数据集
    网易汽车 http://auto.163.com语料数据集
    腾讯汽车 http://auto.qq.com/语料数据集
    新浪汽车 http://auto.sina.com.cn/语料数据集
    数据堂 http://www.datatang.com/汽车领域字典
    输入法字典搜狗输入法汽车领域字典
    下载: 导出CSV

    表  3  本文提出的算法与其他算法在推荐精确率上的比较及提升百分比

    Table  3  Precision comparison and improvement between our algorithm and other algorithms

    算法$N=5$$N=10$$N=15$$N=20$
    FMM0.0760.0650.0530.042
    46.10 %45.38 %45.92 %44.00 %
    TR0.0830.0720.0590.048
    41.13 %39.50 %39.80 %36.00 %
    RS0.0010.0010.0010.001
    99.29 %99.16 %98.98 %98.67 %
    SCTCMAR0.1180.101 0.0840.065
    16.31 %15.13 %14.29 %13.33 %
    SCTCMAR+0.1410.1190.0980.075
    下载: 导出CSV

    表  4  本文提出的算法与其他算法在推荐召回率上的比较及提升百分比

    Table  4  Recall comparison and improvement between our algorithm and other algorithms

    算法$N=5$$N=10$$N=15$$N=20$
    FMM0.3110.4250.6040.672
    43.04 %35.80 %29.52 %26.56 %
    TR0.4050.5010.7050.783
    25.82 %24.32 %17.74 %14.43 %
    RS0.0100.0160.0230.032
    98.17 %97.58 %97.32 %96.50 %
    SCTCMAR0.4720.5930.7980.864
    13.55 %10.42 %6.88 %5.57 %
    SCTCMAR+0.5460.6620.8570.915
    下载: 导出CSV

    表  5  各算法在平均倒序排名上的对比

    Table  5  ARHR comparison between our algorithm and other algorithms

    算法$N=5$$N=10$$N=15$$N=20$
    FMM0.0820.1060.1220.131
    TR0.1190.1370.1480.154
    RS0.0020.0030.0050.006
    SCTCMAR0.1710.1820.2010.210
    SCTCMAR+0.1960.2180.2330.241
    下载: 导出CSV

    表  6  本文算法使用词嵌入模型在平均倒序排名上的影响

    Table  6  ARHR comparison between our algorithm with pre-trained word embedding model

    算法pikamianbaosmallcompact
    SCTCMAR0.1710.1690.1840.186
    SCTCMAR+0.1880.1830.1800.176
    提升(%)9.047.65${\bf-2.22}$${\bf-5.68}$
    下载: 导出CSV
  • [1] Felfernig A, Burke R. Constraint-based recommender systems:technologies and research issues. In:Proceedings of the 10th International Conference on Electronic Commerce. Innsbruck, Austria:ACM, 2008. https://www.researchgate.net/publication/221550593_Constraint-based_recommender_systems_technologies_and_research_issues
    [2] Feng H, Qian X M. Recommendation via user's personality and social contextual. In:Proceedings of the 22nd ACM International Conference on Information and Knowledge Management. San Francisco, California, USA:ACM, 2013. 1521-1524
    [3] Jamali M, Ester M. A matrix factorization technique with trust propagation for recommendation in social networks. In:Proceedings of the 4th ACM Conference on Recommender Systems. Barcelona, Spain:ACM, 2010. 135-142 https://www.researchgate.net/publication/221141035_A_matrix_factorization_technique_with_trust_propagation_for_recommendation_in_social_networks
    [4] 潘涛涛, 文锋, 刘勤让.基于矩阵填充和物品可预测性的协同过滤算法.自动化学报, 2017, 43(9):1597-1606 http://www.aas.net.cn/CN/abstract/abstract19136.shtml

    Pan Tao-Tao, Wen Feng, Liu Qin-Rang. Collaborative filtering recommendation algorithm based on rating matrix filling and item predictability. Acta Automatica Sinica, 2017, 43(9):1597-1606 http://www.aas.net.cn/CN/abstract/abstract19136.shtml
    [5] Yang X W, Steck H, Liu Y. Circle-based recommendation in online social networks. In:Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Beijing, China:ACM, 2012. 1267-1275 https://www.researchgate.net/publication/254464202_Circle-based_recommendation_in_online_social_networks
    [6] Salakhutdinov R, Mnih A. Probabilistic matrix factorization. In:Proceedings of the 20th International Conference on Neural Information Processing Systems. Vancouver, British Columbia, Canada:ACM, 2008. 1257-1264
    [7] Jiang M, Cui P, Liu R, Yang Q. Social contextual recommendation. In:Proceedings of the 21st ACM international Conference on Information and Knowledge Management. Maui, Hawaii, USA:ACM, 2012. 45-54
    [8] 张燕平, 张顺, 钱付兰, 张以文.基于用户声誉的鲁棒协同推荐算法.自动化学报, 2015, 41(5):1004-1012 http://www.aas.net.cn/CN/abstract/abstract18674.shtml

    Zhang Yan-Ping, Zhang Shun, Qian Fu-Lan, Zhang Yi-Wen. Robust collaborative recommendation algorithm based on user's reputation. Acta Automatica Sinica, 2015, 41(5):1004-1012 http://www.aas.net.cn/CN/abstract/abstract18674.shtml
    [9] Qian X M, Feng H, Zhao G S, Mei T. Personalized recommendation combining user interest and social circle. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(7):1763-1777 doi: 10.1109/TKDE.2013.168
    [10] Wang Z, Sun L F, Zhu W W, Yang S Q, Li H Z, Wu D P. Joint social and content recommendation for user-generated videos in online social network. IEEE Transactions on Multimedia, 2013, 15(3):698-709 doi: 10.1109/TMM.2012.2237022
    [11] Wang C, Blei D M. Collaborative topic modeling for recommending scientific articles. In:Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Diego, California, USA:ACM, 2011. 448-456 https://www.researchgate.net/publication/221654332_Collaborative_topic_modeling_for_recommending_scientific_articles
    [12] Ling G, Lyu M R, King I. Ratings meet reviews, a combined approach to recommend. In:Proceedings of the 8th ACM Conference on Recommender Systems. Foster City, Silicon Valley, California, USA:ACM, 2014. 105-112
    [13] McAuley J, Leskovec J. Hidden factors and hidden topics:understanding rating dimensions with review text. In:Proceedings of the 7th ACM Conference on Recommender Systems. Hong Kong, China:ACM, 2013. 165-172 https://www.researchgate.net/publication/262205718_Hidden_factors_and_hidden_topics_Understanding_rating_dimensions_with_review_text
    [14] Wang H, Wang N Y, Yeung D Y. Collaborative deep learning for recommender systems. In:Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Sydney, NSM, Australia:ACM, 2015. 1235-1244 http://www.oalib.com/paper/4068730#.XJ7Jlvk6uPI
    [15] Lecun Y, Bottou L, Bengio Y, Haffner F. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 1998, 86(11):2278-2324 doi: 10.1109/5.726791
    [16] 孙晓, 潘汀, 任福继.基于ROI-KNN卷积神经网络的面部表情识别.自动化学报, 2016, 42(6) 883-891 http://www.aas.net.cn/CN/abstract/abstract18879.shtml

    Sun Xiao, Pan Ting, Ren Fu-Ji. Facial expression recognition using ROI-KNN deep convolutional neural networks. Acta Automatica Sinica, 2016, 42(6):883-891 http://www.aas.net.cn/CN/abstract/abstract18879.shtml
    [17] Pennington J, Socher R, Manning C D. GloVe:global vectors for word representation. In:Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). Doha, Qatar:EMNLP, 2014. 1532-1543
    [18] Kim D, Park C, Oh J, Lee S, Yu H. Convolutional matrix factorization for document context-aware recommendation. In:Proceedings of the 10th ACM Conference on Recommender Systems. Boston, Massachusetts, USA:ACM, 2016. 233-240 https://www.researchgate.net/publication/310818876_Convolutional_Matrix_Factorization_for_Document_Context-Aware_Recommendation
    [19] Glorot X, Bordes A, Bengio Y. Deep sparse rectifier neural networks. In:Proceedings of the 14th International Conference on Artificial Intelligence and Statistics. Lauderdale, USA:AISTATS, 2011. 315-323
    [20] Maas A L, Hannun A Y, Ng A Y. Rectifier nonlinearities improve neural network acoustic models. In:Proceedings of the 30th International Conference on Machine Learning Workshop on Deep Learning for Audio Speech and Language Processing. Atlanta, Georgia, USA:ICML, 2013.
    [21] Deshpande M, Karypis G. Item-based top-N recommendation algorithms. ACM Transactions on Information Systems (TOIS), 2004, 22(1):143-177 doi: 10.1145/963770
    [22] Si L, Jin R. Flexible mixture model for collaborative filtering. In:Proceedings of the 20th International Conference on Machine Learning (ICML-03). Washington, DC, USA:ICML, 2003. 704-711
    [23] Zou H T, Gong Z G, Zhang N, Zhao W, Guo J Z. TrustRank:a Cold-Start tolerant recommender system. Enterprise Information Systems, 2015, 9(2):117-138 doi: 10.1080/17517575.2013.804587
    [24] 奚雪峰, 周国栋.面向自然语言处理的深度学习研究.自动化学报, 2016, 42(10):1445-1465 http://www.aas.net.cn/CN/abstract/abstract18934.shtml

    Xi Xue-Feng, Zhou Guo-Dong. A survey on deep learning for natural language processing. Acta Automatica Sinica, 2016, 42(10):1445-1465 http://www.aas.net.cn/CN/abstract/abstract18934.shtml
    [25] 贺昱曜, 李宝奇.一种组合型的深度学习模型学习率策略.自动化学报, 2016, 42(6):953-958 http://www.aas.net.cn/CN/abstract/abstract18886.shtml

    He Yu-Yao, Li Bao-Qi. A combinatory form learning rate scheduling for deep learning model. Acta Automatica Sinica, 2016, 42(6):953-958 http://www.aas.net.cn/CN/abstract/abstract18886.shtml
  • 加载中
图(6) / 表(6)
计量
  • 文章访问数:  1671
  • HTML全文浏览量:  348
  • PDF下载量:  536
  • 被引次数: 0
出版历程
  • 收稿日期:  2017-05-10
  • 录用日期:  2017-11-06
  • 刊出日期:  2019-03-20

目录

    /

    返回文章
    返回