Predicting response in mobile advertising with Hierarchical Importance-Aware Factorization Machine

• Main Authors: OENTARYO, Richard Jayadi, LIM, Ee Peng, LOW, Jia Wei, LO, David, FINEGOLD, Michael
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2014
• Subjects: Factorization machine; Hierarchy; Importance weight; Mobile advertising; Response prediction; Databases and Information Systems; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/1979; https://ink.library.smu.edu.sg/context/sis_research/article/2978/viewcontent/WSDM14.pdf
• Institution: Singapore Management University

Mobile advertising has recently seen dramatic growth, fueled by the global proliferation of mobile phones and devices. The task of predicting ad response is thus crucial for maximizing business revenue. However, ad response data change dynamically over time, and are subject to cold-start situations...