C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Machine Learning
Personalised Advertising — Exploiting the Distributed User Profile
BT Technology Journal
Web mining for web personalization
ACM Transactions on Internet Technology (TOIT)
Selection of web sites for online advertising using the AHP
Information and Management
Framework for Targeting Banner Advertising On the Internet
HICSS '97 Proceedings of the 30th Hawaii International Conference on System Sciences: Information Systems Track—Internet and the Digital Economy - Volume 4
Scheduling Banner Advertisements on the Web
INFORMS Journal on Computing
Dynamic Conversion Behavior at E-Commerce Sites
Management Science
Minimal knowledge anonymous user profiling for personalized services
IEA/AIE'2005 Proceedings of the 18th international conference on Innovations in Applied Artificial Intelligence
AdROSA-Adaptive personalization of web advertising
Information Sciences: an International Journal
Optimal Scheduling and Placement of Internet Banner Advertisements
IEEE Transactions on Knowledge and Data Engineering
Random Forests for multiclass classification: Random MultiNomial Logit
Expert Systems with Applications: An International Journal
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
An e-customer behavior model with online analytical mining for internet marketing planning
Decision Support Systems
Expert Systems with Applications: An International Journal
Scheduling web banner advertisements with multiple display frequencies
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Real-time bidding for online advertising: measurement and analysis
Proceedings of the Seventh International Workshop on Data Mining for Online Advertising
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Several recent studies have explored the virtues of behavioral targeting and personalization for online advertising. In this paper, we add to this literature by proposing a cost-effective methodology for the prediction of demographic website visitor profiles that can be used for web advertising targeting purposes. The methodology involves the transformation of website visitors' clickstream patterns to a set of features and the training of Random Forest classifiers that generate predictions for gender, age, level of education and occupation category. These demographic predictions can support online advertisement targeting (i) as an additional input in personalized advertising or behavioral targeting, or (ii) as an input for aggregated demographic website visitor profiles that supportmarketingmanagers in selecting websites and achieving an optimal correspondence between target groups and website audience composition. The proposed methodology is validated using data from a Belgian web metrics company. The results reveal that Random Forests demonstrate superior classification performance over a set of benchmark algorithms. Further, the ability of the model set to generate representative demographic website audience profiles is assessed. The stability of the models over time is demonstrated using out-of-period data.