Eager Learning in Two-Stages for Precise and Complete Web Personalization

We present a systematic approach to automatic Web recommender systems based on Web usage mining in a first stage to learn user profiles, and a second data mining phase that is devoted to learning several accurate models for predicting user requests for each profile. Our approach differs from existing methods because it includes two separate learning phases: one to learn the user profiles, and another to learn a recommendation model. Most previous approaches do not include adaptive learning in a separate second phase, and instead base the recommendations on simple assumptions such as nearest profile recommendations, or deployment of pre-discovered association rules