Knowledge and Information Systems

, Volume 49, Issue 1, pp 61–89 | Cite as

Exploring demographic information in social media for product recommendation

  • Wayne Xin Zhao
  • Sui Li
  • Yulan He
  • Liwei Wang
  • Ji-Rong Wen
  • Xiaoming Li
Regular Paper

Abstract

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.

Keywords

E-commerce Product recommendation Product demographic Social media 

Notes

Acknowledgments

The authors thank the anonymous reviewers for their valuable and constructive comments. The work was partially supported by National Natural Science Foundation of China under Grant Nos. 61502502 and 61573026, the pilot project under Baidu open cloud service platform under Grant No. 4333150064, and the National Key Basic Research Program (973 Program) of China under Grant No. 2014CB340403. Xin Zhao was also partially supported by 2015 HTC Young Scholar Program.

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Copyright information

© Springer-Verlag London 2015

Authors and Affiliations

  • Wayne Xin Zhao
    • 1
    • 2
  • Sui Li
    • 4
  • Yulan He
    • 3
  • Liwei Wang
    • 4
  • Ji-Rong Wen
    • 1
    • 2
  • Xiaoming Li
    • 4
  1. 1.School of InformationRenmin University of ChinaBeijingChina
  2. 2.Beijing Key Laboratory of Big Data Management and Analysis MethodsBeijingChina
  3. 3.School of Engineering and Applied ScienceAston UniversityBirminghamUK
  4. 4.School of Electronics Engineering and Computer SciencesPeking UniversityBeijingChina

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