? 基于社交文本信息的跨平台产品购买偏好实验研究
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Journal of Computer Science and Technology 2017, Vol. 32 Issue (4) :828-842    DOI: 10.1007/s11390-017-1763-6
Special Issue on Deep Learning << Previous Articles | >>
基于社交文本信息的跨平台产品购买偏好实验研究
Ting Bai1,2, Student Member, CCF, Hong-Jian Dou1,2,Wayne-Xin Zhao1,2,3,*, Member, CCF, ACM, IEEE, Ding-Yi Yang1, Ji-Rong Wen1,2, Member, CCF, ACM, IEEE
1 School of Information, Renmin University of China, Beijing 100872, China;
2 Beijing Key Laboratory of Big Data Management and Analysis Methods, Beijing 100872, China;
3 Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou 510006, China
An Experimental Study of Text Representation Methods for Cross-Site Purchase Preference Prediction Using the Social Text Data
Ting Bai1,2, Student Member, CCF, Hong-Jian Dou1,2,Wayne Xin Zhao1,2,3,*, Member, CCF, ACM, IEEE, Ding-Yi Yang1, Ji-Rong Wen1,2, Member, CCF, ACM, IEEE
1 School of Information, Renmin University of China, Beijing 100872, China;
2 Beijing Key Laboratory of Big Data Management and Analysis Methods, Beijing 100872, China;
3 Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou 510006, China

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摘要 随着信息科技的发展,越来越多的电子商务网站允许用户使用已有的社交账号登陆。社交媒体平台上有丰富的用户信息和海量的文本信息,探究如何有效利用社交媒体中丰富的用户信息来解决电子商务网站中的冷启动问题,从而改进网站服务,是一个非常具有潜在经济价值的问题。本论文针对跨平台的信息共享:如何有效利用社交媒体平台上的文本信息,对用户购买产品类别偏好进行预测。社交媒体平台有海量的文本信息,如何有效从中挖掘与购买产品相关的信息,是跨平台信息共享的一大挑战。本论文提出两大类文本表示方法,传统的浅层文本表示和利用深度神经网络学习的深层文本表示方法。通过在电子商务网站京东、社交媒体平台新浪微博构建大规模真实跨平台用户数据集,证明了社交文本信息的确能够用来预测用户在电子商务网站上的购买偏好,并且,当预测的产品类别较多时,通过深度神经网络学习的社交文本表示方法预测效果更加显著。
关键词社交媒体   电子商务网站   购买偏好   深度神经网络     
Abstract: Nowadays, many e-commerce websites allow users to login with their existing social networking accounts. When a new user comes to an e-commerce website, it is interesting to study whether the information from external social media platforms can be utilized to alleviate the cold-start problem. In this paper, we focus on a specific task on cross-site information sharing, i.e., leveraging the text posted by a user on the social media platform (termed as social text) to infer his/her purchase preference of product categories on an e-commerce platform. To solve the task, a key problem is how to effectively represent the social text in a way that its information can be utilized on the e-commerce platform. We study two major kinds of text representation methods for predicting cross-site purchase preference, including shallow textual features and deep textual features learned by deep neural network models. We conduct extensive experiments on a large linked dataset, and our experimental results indicate that it is promising to utilize the social text for predicting purchase preference. Specially, the deep neural network approach has shown a more powerful predictive ability when the number of categories becomes large.
Keywordssocial media   e-commerce website   purchase preference   deep neural network     
Received 2016-12-21;
本文基金:

This work was partially supported by the National Natural Science Foundation of China under Grant Nos. 61502502 and 61502501, the National Basic Research 973 Program of China under Grant No. 2014CB340403 and the Beijing Natural Science Foundation under Grant No. 4162032. Ting Bai was supported by the Outstanding Innovative Talents Cultivation Funded Programs 2016 of Renmin Univertity of China. Wayne Xin Zhao was also supported by the Opening Project of Guangdong Province Key Laboratory of Big Data Analysis and Processing under Grant No. 2017001.

通讯作者: Wayne-Xin Zhao     Email: batmanfly@gmail.com
About author: Ting Bai is currently a Ph.D. student at the School of Information, Renmin University of China, Beijing. She is working in Beijing Key Laboratory of Big Data Management and Analysis Methods, Beijing. Her research mainly focuses on social content analysis, recommender systems and deep learning, especially on commercial intent detection and human behavior analysis.
引用本文:   
Ting Bai, Hong-Jian Dou, Wayne Xin Zhao, Ding-Yi Yang, Ji-Rong Wen.基于社交文本信息的跨平台产品购买偏好实验研究[J]  Journal of Computer Science and Technology , 2017,V32(4): 828-842
Ting Bai, Hong-Jian Dou, Wayne Xin Zhao, Ding-Yi Yang, Ji-Rong Wen.An Experimental Study of Text Representation Methods for Cross-Site Purchase Preference Prediction Using the Social Text Data[J]  Journal of Computer Science and Technology, 2017,V32(4): 828-842
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