标题:Short text based cooperative classification for multiple platforms
作者:Li, Mingzhu ;Chen, Lihua ;Liu, Tianyuan ;Sun, Yuqing
通讯作者:Sun, Yuqing
作者机构:[Li, Mingzhu ] School of Computer Science and Technology, Shandong University, Jinan, China;[Liu, Tianyuan ;Sun, Yuqing ] School of Software College, 更多
会议名称:23rd IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2019
会议日期:6 May 2019 through 8 May 2019
来源:Proceedings of the 2019 IEEE 23rd International Conference on Computer Supported Cooperative Work in Design, CSCWD 2019
出版年:2019
页码:87-92
DOI:10.1109/CSCWD.2019.8791500
关键词:Hierarchy; Product classification; Topic model
摘要:With the popularity of electronic commerce, there are increasing requirements on product comparison services, which collect the similar products information on different platforms for a user reference. Since there are a large quantity of products on each platform, it is necessary to classify the products based on their short descriptions and to learn the relationships between the different categories on multiple platforms. In this paper, we propose the Rectified Topic Classification model to classify products into hierarchical categories based on their short text descriptions. We adopt the topic model to capture the latent features of products from the noisy short descriptions generated by merchants. To reduce the uncertainty of the inferring topic features of a new product, we invoke the topic model several times to get a set of probabilistic feature results and adopt the convolutional neural network for classification. To learn the correlations between two platform categories, the mapping matrix is learned by using a set of seed products. We crawled several real datasets from popular e-commerce platforms and perform experiments to verify our methods. The results show that our method outperforms the related methods. © 2019 IEEE.
收录类别:EI;SCOPUS
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85071499607&doi=10.1109%2fCSCWD.2019.8791500&partnerID=40&md5=723f99e1daec07b01cbee40de95553dc
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