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 produc...

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Bibliographic Details
Published in:Knowledge and information systems 2016-10, Vol.49 (1), p.61-89
Main Authors: Zhao, Wayne Xin, Li, Sui, He, Yulan, Wang, Liwei, Wen, Ji-Rong, Li, Xiaoming
Format: Article
Language:English
Subjects:
Big Data
Computer Science
Data Mining and Knowledge Discovery
Database Management
Demographics
Digital media
Electronic commerce
Information Storage and Retrieval
Information Systems and Communication Service
Information Systems Applications (incl.Internet)
Internet
IT in Business
Performance evaluation
Product reviews
Real time
Recommendations
Recommender systems
Regression analysis
Regular Paper
Social networks
Studies
User behavior
Websites
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Summary: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.
ISSN:0219-1377
0219-3116
DOI:10.1007/s10115-015-0897-5