| Abstract | Personalized Web Search becomes nowadays a
promising option in the field of Information Retrieval and
search engines design by improving both output quality and
user experience. In this paper, we present and evaluate the
subsystem, which conducts the Advanced and Personalized
search of PeRSSonal, a web-based mechanism for the retrieval,
processing and presentation of articles and RSS feeds collected
from major news portals of the Internet. The proposed
technique uses information explicitly provided by the user in
his profile as well as information that the mechanism can learn
from the user?s behavior during his search and browsing
sessions in the system. As this behavior dynamically evolves,
the same happens to the user?s interests under the prism of the
search engine. By adopting this user-centric approach, we
manage to present the user with better-refined and more
focused results, incorporating his personal preferences to the
output. The algorithm operates not in a stand-alone manner
but it co-operates and binds with the rest of modules of
PeRSSonal in order to accomplish maximum integration with
the system. Furthermore, we introduce an enhancement in the
search function, based on cached results from past search
sessions of each user individually.
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