Learning and Revising User Profiles: The Identification ofInteresting Web Sites
Machine Learning - Special issue on multistrategy learning
Effective personalization based on association rule discovery from web usage data
Proceedings of the 3rd international workshop on Web information and data management
Probabilistic Model Estimation for Collaborative Filtering Based on Items Attributes
WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
Clustering for probabilistic model estimation for CF
WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web
A probabilistic music recommender considering user opinions and audio features
Information Processing and Management: an International Journal - Special issue: AIRS2005: Information retrieval research in Asia
Computational Intelligence techniques for Web personalization
Web Intelligence and Agent Systems
Web personalization: my own web based on open content platform
WISE'05 Proceedings of the 6th international conference on Web Information Systems Engineering
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We present a Context Ultra-Sensitive Approach based on two-step Recommender systems (CUSA-2-step-Rec). Our approach relies on a committee of profile-specific neural networks. This approach provides recommendations that are accurate and fast to train because only the URLs relevant to a specific profile are used to define the architecture of each network. We compare the proposed approach with collaborative filtering showing that our approach achieves higher coverage and precision while being faster, and requiring lower main memory at recommendation time. While most recommenders are inherently context sensitive, our approach is context ultra-sensitive because a different recommendation model is designed for each profile separately.