Exploring demographic information in social media for product recommendation


In many e-commerce Web sites, product recommendation is essential to improve user experience and boost sales. Most existing product recommender systems rely on historical transaction records or Web-site-browsing history of consumers in order to accurately predict online users’ preferences for product recommendation. As such, they are constrained by limited information available on specific e-commerce Web sites. With the prolific use of social media platforms, it now becomes possible to extract product demographics from online product reviews and social networks built from microblogs. Moreover, users’ public profiles available on social media often reveal their demographic attributes such as age, gender, and education. In this paper, we propose to leverage the demographic information of both products and users extracted from social media for product recommendation. In specific, we frame recommendation as a learning to rank problem which takes as input the features derived from both product and user demographics. An ensemble method based on the gradient-boosting regression trees is extended to make it suitable for our recommendation task. We have conducted extensive experiments to obtain both quantitative and qualitative evaluation results. Moreover, we have also conducted a user study to gauge the performance of our proposed recommender system in a real-world deployment. All the results show that our system is more effective in generating recommendation results better matching users’ preferences than the competitive baselines.

Publication DOI: https://doi.org/10.1007/s10115-015-0897-5
Divisions: College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
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Additional Information: The final publication is available at Springer via http://dx.doi.org/10.1080/21552851.2015.1086557
Uncontrolled Keywords: e-commerce,product demographic,product recommendation,social media,Artificial Intelligence,Software,Information Systems,Hardware and Architecture,Human-Computer Interaction
Publication ISSN: 0219-3116
Last Modified: 22 Jul 2024 07:07
Date Deposited: 09 Nov 2015 15:05
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Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2016-10
Published Online Date: 2015-10-23
Accepted Date: 2015-10-10
Authors: Zhao, Wayne Xin
Li, Sui
He, Yulan (ORCID Profile 0000-0003-3948-5845)
Wang, Liwei
Wen, Ji-Rong
Li, Xiaoming



Version: Accepted Version

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