article

Exploring demographic information in social media for product recommendation

Authors:

Wayne Xin Zhao,

Xiaoming LiAuthors Info & Claims

Knowledge and Information Systems, Volume 49, Issue 1

Pages 61 - 89

https://doi.org/10.1007/s10115-015-0897-5

Published: 01 October 2016 Publication History

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.

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Ge YLiu SFu ZTan JLi ZXu SLi YXian YZhang Y(2024)A Survey on Trustworthy Recommender SystemsACM Transactions on Recommender Systems10.1145/3652891Online publication date: 13-Apr-2024
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Jin WMao HLi ZJiang HLuo CWen HHan HLu HWang ZLi RLi ZCheng MGoutam RZhang HSubbian KWang SSun YTang JYin BTang XOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Amazon-M2Proceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3666473(8006-8026)Online publication date: 10-Dec-2023
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Exploring demographic information in social media for product recommendation
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cover image Knowledge and Information Systems

Knowledge and Information Systems Volume 49, Issue 1

October 2016

392 pages

ISSN:0219-1377

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Copyright © Copyright © 2016 Springer-Verlag London.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 October 2016

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Cited By

Ge YLiu SFu ZTan JLi ZXu SLi YXian YZhang Y(2024)A Survey on Trustworthy Recommender SystemsACM Transactions on Recommender Systems10.1145/3652891Online publication date: 13-Apr-2024
https://doi.org/10.1145/3652891
Peng ZZhang HMin F(2024)IUG-CFExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121887238:PBOnline publication date: 27-Feb-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.121887
Jin WMao HLi ZJiang HLuo CWen HHan HLu HWang ZLi RLi ZCheng MGoutam RZhang HSubbian KWang SSun YTang JYin BTang XOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Amazon-M2Proceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3666473(8006-8026)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3666473
Ferrara AAnelli VMancino ADi Noia TDi Sciascio E(2023)KGFlex: Efficient Recommendation with Sparse Feature Factorization and Knowledge GraphsACM Transactions on Recommender Systems10.1145/35889011:4(1-30)Online publication date: 3-Apr-2023
https://dl.acm.org/doi/10.1145/3588901
Parmar JChouhan SRaychoudhury VRathore S(2023)Open-world Machine Learning: Applications, Challenges, and OpportunitiesACM Computing Surveys10.1145/356138155:10(1-37)Online publication date: 2-Feb-2023
https://dl.acm.org/doi/10.1145/3561381
Latha YRao B(2023)Product recommendation using enhanced convolutional neural network for e-commerce platformCluster Computing10.1007/s10586-023-04053-327:2(1639-1653)Online publication date: 2-Jun-2023
https://dl.acm.org/doi/10.1007/s10586-023-04053-3
Hamdollahi Oskouei SHashemzadeh M(2023)FoodRecNet: a comprehensively personalized food recommender system using deep neural networksKnowledge and Information Systems10.1007/s10115-023-01897-465:9(3753-3775)Online publication date: 7-May-2023
https://dl.acm.org/doi/10.1007/s10115-023-01897-4
Alhejaili AFatima S(2022)Expressive Latent Feature Modelling for Explainable Matrix Factorisation-based Recommender SystemsACM Transactions on Interactive Intelligent Systems10.1145/353029912:3(1-30)Online publication date: 26-Jul-2022
https://dl.acm.org/doi/10.1145/3530299
Li TLi YYi-Ping Phoebe C(2021)Novel Multidimensional Collaborative Filtering Algorithm Based on Improved Item Rating PredictionScientific Programming10.1155/2021/25926042021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/2592604
Anelli VDi Noia TDi Sciascio EFerrara AMancino A(2021)Sparse Feature Factorization for Recommender Systems with Knowledge GraphsProceedings of the 15th ACM Conference on Recommender Systems10.1145/3460231.3474243(154-165)Online publication date: 13-Sep-2021
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