Demographic prediction based on user's browsing behavior

By Jian Hu, Hua-Jun Zeng, Hua Li, Chenglei Niu, Zheng Chen. The Web Conference, 2007

298 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/3d94df47eed3628ed69b3306bf7557ee9f66349f

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Citations in the corpus

Who cares about your Facebook friends? Credit scoring for microfinance (2015, background): “[26] predict demographic attributes and Raeder et al.”
Mining target users for online marketing based on App Store data (2015, unknown): “Hu et al [11] used user’s browsing behaviors to predict demographic information like age and gender.”
Who Does What on the Web: A Large-Scale Study of Browsing Behavior (2012, background): “Similarly, using one week of activity for 189,000 users, Hu et al. (2007) were able to reliably infer limited demographic information from the content of visited sites.”
Private traits and attributes are predictable from digital records of human behavior (2013, background): “It has been shown that age, gender, occupation, education level, and even personality can be predicted from people ’ s Web site browsing logs (11 – 15).”
Dimensionality Reduction via Matrix Factorization for Predictive Modeling from Large, Sparse Behavioral Data (2015, background): “The reasons are generally the same that we have encountered before: to reduce the noise in the data (Hu et al. 2007); to make the dimensionality of the space more tractable (Thorleuchter et al.”
Measuring Price Discrimination and Steering on E-commerce Web Sites (2014, methodology): “While these techniques typically use click histories for personalization, other features have also been used, including geolocation [2, 53, 54] and demographics (typically inferred from search and browsing histories) [18, 47].”
Measuring personalization of web search (2013, background): “Studies have shown that user demographics can be reliably inferred from browsing histories, which can be useful for personalizing content [18, 28].”