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...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.展开更多
Image registration is an indispensable component in multi-source remote sensing image processing. In this paper, we put forward a remote sensing image registration method by including an improved multi-scale and multi...Image registration is an indispensable component in multi-source remote sensing image processing. In this paper, we put forward a remote sensing image registration method by including an improved multi-scale and multi-direction Harris algorithm and a novel compound feature. Multi-scale circle Gaussian combined invariant moments and multi-direction gray level co-occurrence matrix are extracted as features for image matching. The proposed algorithm is evaluated on numerous multi-source remote sensor images with noise and illumination changes. Extensive experimental studies prove that our proposed method is capable of receiving stable and even distribution of key points as well as obtaining robust and accurate correspondence matches. It is a promising scheme in multi-source remote sensing image registration.展开更多
文摘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.
基金supported by National Nature Science Foundation of China (Nos. 61462046 and 61762052)Natural Science Foundation of Jiangxi Province (Nos. 20161BAB202049 and 20161BAB204172)+2 种基金the Bidding Project of the Key Laboratory of Watershed Ecology and Geographical Environment Monitoring, NASG (Nos. WE2016003, WE2016013 and WE2016015)the Science and Technology Research Projects of Jiangxi Province Education Department (Nos. GJJ160741, GJJ170632 and GJJ170633)the Art Planning Project of Jiangxi Province (Nos. YG2016250 and YG2017381)
文摘Image registration is an indispensable component in multi-source remote sensing image processing. In this paper, we put forward a remote sensing image registration method by including an improved multi-scale and multi-direction Harris algorithm and a novel compound feature. Multi-scale circle Gaussian combined invariant moments and multi-direction gray level co-occurrence matrix are extracted as features for image matching. The proposed algorithm is evaluated on numerous multi-source remote sensor images with noise and illumination changes. Extensive experimental studies prove that our proposed method is capable of receiving stable and even distribution of key points as well as obtaining robust and accurate correspondence matches. It is a promising scheme in multi-source remote sensing image registration.
