@inbook {3146,
	title = {Learning from user{\textquoteright}s behavior - A technique for personalized news search supporting result cashing},
	booktitle = {Information Extraction from the Internet},
	year = {2011},
	pages = { 177-196},
	publisher = {iConcept Press},
	organization = {iConcept Press},
	chapter = {11},
	author = {Christos Bouras and Vassilis Poulopoulos and Panagiotis Silintziris}
}
@conference {2951,
	title = {Date - based dynamic caching mechanism},
	booktitle = {IADIS European Conference on Data Mining 2009, Algavre, Portugal},
	year = {2009},
	month = {June 18 - 20},
	pages = {67-74},
	abstract = {News portals based on the RSS protocol are becoming nowadays one of the dominant ways that Internet users follow in order to locate the information they are looking for. Search engines, which operate at the back-end of a big portion of these web sites, receive millions of queries per day on any and every walk of web life. While these queries are submitted by thousands of unrelated users, studies have shown that small sets of popular queries account for a significant fraction of the query stream. A second truth has to do with the high frequency that a particular user tends to submit the same or highly relative search requests to the engine. By combining these facts, in this paper, we design and analyze the caching algorithm deployed in our personalized RSS portal, a web-based mechanism for the retrieval, processing and presentation in a personalized view of articles and RSS feeds collected from major Internet news portals. By using moderate amounts of memory and little computational overhead, we achieve to cache query results in the server, both for personalized and non-personalized user searches. Our caching algorithm operates not in a stand-alone manner but it co-operates and binds with the rest of the modules of our Portal in order to accomplish maximum integration with the system.},
	author = {Christos Bouras and Vassilis Poulopoulos and Panagiotis Silintziris}
}
@conference {2947,
	title = {Personalized News Search in WWW: Adapting on user{\textquoteright}s behavior},
	booktitle = {The Fourth International Conference on Internet and Web ASpplications and Services - ICIW 2009, Venice, Italy},
	year = {2009},
	month = {24 - 28 May},
	pages = { 125 - 130},
	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.},
	author = {Christos Bouras and Vassilis Poulopoulos and Panagiotis Silintziris}
}
