  Personalization is being applied to great extend in many systems. This paper
presents a multi-dimensional user data model and its application in web search.
Online and Offline activities of the user are tracked for creating the user
model. The main phases are identification of relevant documents and the
representation of relevance and similarity of the documents. The concepts
Keywords, Topics, URLs and clusters are used in the implementation. The
algorithms for profiling, grading and clustering the concepts in the user model
and algorithm for determining the personalized search results by re-ranking the
results in a search bank are presented in this paper. Simple experiments for
evaluation of the model and their results are described.
