Exploring demographic information in social media for product recommendation. Issue 1 (October 2016)

• Record Type: Journal Article
• Authors: Zhao, Wayne; Li, Sui; He, Yulan; Wang, Liwei; Wen, Ji-Rong; Li, Xiaoming
• Is Part Of: Knowledge and information systems. Volume 49:Issue 1(2016:Oct.)
• Page Start: 61
• Page End: 89
• Publication Date: 2016-10
• Subjects: E-commerce -- Product recommendation -- Product demographic -- Social media; Expert systems (Computer science) -- Periodicals; Information storage and retrieval systems -- Periodicals; 006.33
• DOI: 10.1007/s10115-015-0897-5
• Languages: English
• ISSNs: 0219-1377

Abstract

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.