Predicting users' purchasing behaviors using their browsing history

He, Tieke, Yin, Hongzhi, Chen, Zhenyu, Zhou, Xiaofang and Luo, Bin (2015). Predicting users' purchasing behaviors using their browsing history. In: Mohamed A. Sharaf, Muhammad Aamir Cheema and Jianzhong Qi, Databases Theory and Applications. 26th Australasian Database Conference (ADC), Melbourne, Australia, (129-141). 4-7 June 2015. doi:10.1007/978-3-319-19548-3_11

Author He, Tieke
Yin, Hongzhi
Chen, Zhenyu
Zhou, Xiaofang
Luo, Bin
Title of paper Predicting users' purchasing behaviors using their browsing history
Conference name 26th Australasian Database Conference (ADC)
Conference location Melbourne, Australia
Conference dates 4-7 June 2015
Proceedings title Databases Theory and Applications   Check publisher's open access policy
Journal name Databases Theory and Applications   Check publisher's open access policy
Series Lecture Notes in Computer Science
Place of Publication Heidelberg, Germany
Publisher Springer
Publication Year 2015
Sub-type Fully published paper
DOI 10.1007/978-3-319-19548-3_11
Open Access Status Not Open Access
ISBN 9783319195476
ISSN 0302-9743
Editor Mohamed A. Sharaf
Muhammad Aamir Cheema
Jianzhong Qi
Volume 9093
Start page 129
End page 141
Total pages 13
Collection year 2016
Language eng
Abstract/Summary Some E-commerce giants (e.g., Amazon and Jingdong) with abundant purchasing data achieve highly accurate recommendations, since people have to pay for their choices and their purchasing behaviors are more qualified and valid for capturing users’ needs and preferences than other types of users’ behavior data (e.g., browsing). However, there is not enough users’ purchasing data available for most of small and medium-size E-commerce sites as well as some newly established E-commerce sites. In this paper, we aim to alleviate the sparsity of users’ purchasing data by exploiting users’ browsing data which is more sufficient. The low validity and reliability of users’ browsing data raises great challenge for accurately predicting users’ purchasing behaviors since there are many factors leading to users’ browsing behaviors. To this end, we propose a novel semi-supervised method to make the most of both high-quality purchasing data and low-quality browsing data to predict users’ purchasing behaviors. Specifically, we first use a small amount of purchasing data to supervise the model training of browsing data, and then integrate the results into the item-based collaborative filtering method. We conduct extensive experiments on a real dataset, and the experimental results show the superiority of our method by achieving 25% improvements over traditional collaborative-filtering methods.
Keyword Recommender systems
Data sparsity
Small medium E-commerce
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

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