Interactive cluster-based personalized retrieval on large document collections

dc.contributor.author	Μπέλσης, Πέτρος	el
dc.contributor.author	Κωνσταντόπουλος, Χαράλαμπος	el
dc.contributor.author	Μάμαλης, Βασίλης	el
dc.contributor.author	Πάντζιου, Γραμματή Ε.	el
dc.contributor.author	Σκουρλάς, Χρήστος Π.	el
dc.date.accessioned	2015-05-26T20:23:58Z
dc.date.available	2015-05-26T20:23:58Z
dc.date.issued	2015-05-26
dc.identifier.uri	http://hdl.handle.net/11400/11209
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.source	http://link.springer.com/chapter/10.1007%2F978-3-540-68127-4_22	en
dc.subject	Μεγάλες συλλογές εγγράφων
dc.subject	Εξατομικευμένη ανάκτηση
dc.subject	συλλογή περιεχομένου πολυμέσων
dc.subject	εξατομικευμένες υπηρεσίες
dc.subject	ομαδοποίηση έγγραφων
dc.subject	Large Document Collections
dc.subject	Personalized Retrieval
dc.subject	multimedia content collection
dc.subject	personalized services
dc.subject	Document clustering
dc.title	Interactive cluster-based personalized retrieval on large document collections	en
heal.type	conferenceItem
heal.classification	Τεχνολογία
heal.classification	Πληροφορική
heal.classification	Technology
heal.classification	Computer science
heal.classificationURI	N/A-Τεχνολογία
heal.classificationURI	N/A-Πληροφορική
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh85133147
heal.classificationURI	http://skos.um.es/unescothes/C00750
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh2007004888
heal.identifier.secondary	DOI: 10.1007/978-3-540-68127-4_22
heal.language	en
heal.access	campus
heal.recordProvider	Τεχνολογικό Εκπαιδευτικό Ίδρυμα Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Πληροφορικής Τ.Ε.	el
heal.publicationDate	2008
heal.bibliographicCitation	Belsis, P., Konstantopoulos, C., Mamalis, B., Pantziou, G. and Skourlas, C. (2008) Interactive Cluster-Based Personalized Retrieval on Large Document Collections. Proceedings of 1st International Symposium on Intelligent Interactive Multimedia Systems and Services (KES-IIMSS-08). Springer Berlin Heidelberg	en
heal.abstract	Lately, many systems and websites add personalization functionalities among their provided services. However, for large document collections it is difficult for the user to direct effective queries from the beginning of his/her search, since accurate query terms may not be known in advance. In this paper we describe a system that applies a hybrid approach to assist a user identify the most relevant documents: at the beginning it applies dynamic personalization techniques based on user modeling to initiate the search on a large document and multimedia content collection; next the query is further refined using a clustering based approach which after processing a sub-collection of documents presents the user with more categories to select from a list of new keywords. We analyze the most prominent implementation choices for the modular components of the proposed architecture: a machine learning approach for personalized services, a clustering based approach towards a user directed query refinement and a parallel processing module that supports document clustering in order to decrease the system’s response times.	en
heal.publisher	Springer Berlin Heidelberg	en
heal.fullTextAvailability	true
heal.conferenceName	Proceedings of 1st International Symposium on Intelligent Interactive Multimedia Systems and Services (KES-IIMSS-08)	en
heal.conferenceItemType	full paper