Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
5E: A Framework to Yield High Performance in Real-Time Data Mining over the Internet
HPC '00 Proceedings of the The Fourth International Conference on High-Performance Computing in the Asia-Pacific Region-Volume 2 - Volume 2
A Study on the Comparison between Content-Based and Preference-Based Recommendation Systems
SKG '08 Proceedings of the 2008 Fourth International Conference on Semantics, Knowledge and Grid
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Real-Time Data Mining Methodology and a Supporting Framework
NSS '09 Proceedings of the 2009 Third International Conference on Network and System Security
Classification Using Streaming Random Forests
IEEE Transactions on Knowledge and Data Engineering
Supporting smart interactions with predictive analytics
The smart internet
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The traditional paradigm for Web interactions, where the interactions are server-driven rather than user-driven, has limitations that are becoming increasingly apparent. The Personal Web proposes to provide intelligent services that support a more user-centric interaction paradigm in order to allow the user to more easily assemble and aggregate web elements to accomplish specific tasks. In this paper we examine the role predictive analytics can play in intelligent services supporting decision-making tasks and describe the Predictive Analytics in Smart Interactions Framework (PASIF), which is a framework for incorporating predictive analytics into intelligent services. PASIF achieves effective levels of support in the dynamic real-time environment of the Personal Web by incorporating ensemble models and techniques to detect and adapt to concept drift in the data sources.