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融合社交因素和评论文本卷积网络模型的汽车推荐研究 预览

Social and Comment Text CNN Model Based Automobile Recommendation
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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266 995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. This paper mainly studies the influence of social environment and comment text on the user decision making process in purchasing an automobile, and proposes an automobile recommendation model named SCTCMAR(social and comment text CNN model based automobile recommendation). First, SCTCMAR defines a social circle of users in terms of user’s purpose in choosing an automobile;on this basis, a method to calculate personal preference is put forward. and preference similarity is put forward. Second, a convolution neural network model is designed to learn the hidden features from the automobile comment texts. Afterwards, both the social circle and comment text features are integrated into the recommendation model, and low-rank matrix decomposition technology is used to solve the problem. In addition,by applying the GloVe pre-training word embedded model to SCTCMAR, an improved version, SCTCMAR+ is further proposed. Finally, a performance of comparison SCTCMAR+, SCTCMAR with FMM(flexible mixture model), TR(trust rank), and Random sampling is performed on a real dataset with 266 995 users and 702 automobiles. Experimental results show that SCTCMAR+ and SCTCMAR models outperform other counterparts in precision, recall and ARHR(average reciprocal hit rank).
作者 冯永 陈以刚 强保华 FENG Yong;CHEN Yi-Gang;QIANG Bao-Hua(College of Computer Science,Chongqing University,Chong-qing 400030;Key Laboratory of Dependable Service Computing in Cyber Physical Society,Ministry of Education,Chong-qing University,Chongqing 400030;Guangxi Cooperative Innovation Center of Cloud Computing and Big Data,Guilin University of Electronic Technology,Guilin 541004)
出处 《自动化学报》 EI CSCD 北大核心 2019年第3期518-529,共12页 Acta Automatica Sinica
基金 国家自然科学基金(61762025) 国家重点研究发展计划(2017YFB1402400) 重庆市基础与前沿研究计划(cstc2017jcyjAX0340) 广西可信软件重点实验室开放课题(kx201701) 广西云计算与大数据协同创新中心开放课题(YD16E01) 重庆市重点产业共性关键技术创新专项(cstc2017zdcy-zdyxx0047) 重庆市社会事业与民生保障科技创新专项(cstc2017shmsA20013)资助.
关键词 汽车推荐 卷积神经网络 社交圈 矩阵分解 Automobile recommendation convolution neural network(CNN) social circle matrix factorization
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;通信作者: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.E-mail:fengyong@cqu.edu.cn.
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  • 1高建煌,陈恩红,刘淇.基于用户兴趣传播的协同过滤方法[J].电子技术(上海),2010(6):1-4. 被引量:1
  • 2陈刚,刘发升.基于BP神经网络的数据挖掘方法[J].计算机与现代化,2006(10):20-22. 被引量:9
  • 3Resnick P, Iakovou N, Sushak M, Bergstrom P, Riedl J. GroupLens: an open architecture for collaborative filtering of netnews. In: Proceedings of the 1994 Computer Supported Cooperative Work. Chapel Hill: ACM, 1994. 175-186. 被引量:1
  • 4Hill W C, Stead L, Rosenstein M, Furnas G W. Recommending and evaluating choices in a virtual community of use. In: Proceedings of the 1995 SIGCHI Conference on Human Factors in Computing Systems. Denver: ACM, 1995. 194-201. 被引量:1
  • 5Lam S K, Riedl J. Shilling recommender systems for fun and profit. In: Proceedings of the 13th International Conference on World Wide Web. New York, USA: ACM, 2004. 393-402. 被引量:1
  • 6O'Mahony M P, Hurley N J, Kushmerick N, Silvestre G C M. Collaborative recommendation: a robustness analysis. ACM Transactions on Internet Technology (TOIT), 2004, 4(4): 344-377. 被引量:1
  • 7Mobasher B, Burke R, Sandvig J J. Model-based collaborative filtering as a defense against profile injection attacks. In: Proceedings of the 21st National Conference on Artificial Intelligence and the 18th Innovative Applications of Artificial Intelligence Conference. Boston, Massachusetts, USA: AAAI, 2006. 被引量:1
  • 8Gunes I, Kaleli C, Bilge A, Polat H. Shilling attacks against recommender systems: a comprehensive survey. Artificial Intelligence Review, 2014, 42(4): 767-799. 被引量:1
  • 9Mobasher B, Burke R, Williams C, Bhaumik R. Analysis and detection of segment-focused attacks against collaborative recommendation. In: Proceedings of the 7th International Workshop on Knowledge Discovery on the Web. Chicago, IL: Springer Berlin Heidelberg, 2006. 96-118. 被引量:1
  • 10Burke R D, Mobasher B, Williams C, Bhaumik R. Classification features for attack detection in collaborative recommender systems. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Philadelphia, PA, USA: ACM, 2006. 542-547. 被引量:1

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