© September 2000 George Paliouras, All Rights Reserved1 Personalized Information Access George Paliouras Christos Papatheodorou Vangelis Karkaletsis Constantine.

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1 © September 2000 George Paliouras, All Rights Reserved1 Personalized Information Access George Paliouras Christos Papatheodorou Vangelis Karkaletsis Constantine D. Spyropoulos NCSR “Demokritos” Email: paliourg@iit.demokritos.grpaliourg@iit.demokritos.gr WWW: http://www.iit.demokritos.gr/skelhttp://www.iit.demokritos.gr/skel

2 © September 2000 George Paliouras, All Rights Reserved2 WWW: the new face of the Net Once upon a time, the Internet was a forum for exchanging information. Then … …came the Web. The Web introduced new capabilities … …and attracted many more people … …increasing commercial interest … …and turning the Net into a real forum …

3 © September 2000 George Paliouras, All Rights Reserved3 Services on the Internet Information providers are still the majority… CommercialNon-Commercial CNNReuters TimesYahoo CORDISNCSTRL MLNETLibrary

4 © September 2000 George Paliouras, All Rights Reserved4 •We have looked at three different types: –Information filtering (profiles) –Information retrieval (queries) –Navigation Information access •The Web has introduced new ways to access information. passive active •… covering the majority of today’s information services.

5 © September 2000 George Paliouras, All Rights Reserved5 Personalized information access •Adaptation of the system to the user. •Social motivation: –Better service for the citizen (reduction of the information overload). •Commercial motivation: –Customer relationship management (targeted advertisement, customer retention, increased sales, etc.)

6 © September 2000 George Paliouras, All Rights Reserved6 Personalized information access sources server receivers

7 © September 2000 George Paliouras, All Rights Reserved7 User modeling •The process of constructing models that can be used to adapt the system to the user’s requirements. •Types of user requirement: –Interests (e.g. sports and finance articles) –Knowledge level (e.g. novice – expert) –Preferences (e.g. appearance of GUI) –etc.

8 © September 2000 George Paliouras, All Rights Reserved8 User Models •User model (type A): [PERSONAL] User x -> sports, stock market •User model (type B): [PERSONAL] User x, Age 26, Male -> sports, stock market •User community: [GENERIC] Users {x,y,z} -> sports, stock market •User sterotype: [GENERIC] Users {x,y,z}, Age [20..30], Male -> sports, stock market

9 © September 2000 George Paliouras, All Rights Reserved9 Collaborative filtering •Memory-based “learning”, (e.g. k-nn): –Given a group of users… –…and a new user… –…find similar users. •Already in commercial use (e.g. Firefly, amazon.com ) •Problem: It does not give any insight about the usage of the system.

10 © September 2000 George Paliouras, All Rights Reserved10 Learning user models User 1 User 2User 3 User 4 User 5 Observation of the users interacting with the system. User models Community 1 Community 2 User communities

11 © September 2000 George Paliouras, All Rights Reserved11 UMIE: User Modeling for Information Extraction

12 © September 2000 George Paliouras, All Rights Reserved12 The UMIE Architecture

13 © September 2000 George Paliouras, All Rights Reserved13 UMIE: Stereotypes and the Domain any person development people product news strategic planners business development company results product news market news financial specialists company results marketing and market research people business development company results product news market news information specialists business development company results product news market news consultants business development company results product news market news personnel news public relation people business development company results product news market news personnel news sales people business development company results product news market news management personnel market news personnel news

14 © September 2000 George Paliouras, All Rights Reserved14 UMIE: A User Model

15 © September 2000 George Paliouras, All Rights Reserved15 ΜΙΤΟΣ: Αναζήτηση, Εξαγωγή και Εξόρυξη Πληροφορίας Υποσύστημα Μοντελοποίησης Πληροφορίας Συνεργαζόμενοι Εταίροι: Πανεπιστήμιο Πειραιώς ΕΚΕΦΕ «Δημόκριτος» SENA Α.Ε. ΚΑΠΑ-TEL A.E.

16 © September 2000 George Paliouras, All Rights Reserved16 Σύστημα Διεπαφής Υποσύστημα Μοντελοποίησης Χρηστών request response action ΜΙΤΟΣ – Μ.Χ.: Γενική άποψη

17 © September 2000 George Paliouras, All Rights Reserved17 ΜΙΤΟΣ: Υποσύστημα Μ.Χ. Ατομικά Μοντέλα Χρηστών Γενικευμένα Μοντέλα Χρηστών Module Επικοινωνίας και Διαχείρισης Module Διαχείρισης Ατομικών Μοντέλων Module Διαχείρισης Γενικευμένων Μοντέλων Κεντρικός Server του ΜΙΤΟΣ

18 © September 2000 George Paliouras, All Rights Reserved18 ΜΙΤΟΣ: Κατασκευή Γενικευμένων Μοντέλων •Στόχος: Ενίσχυση του ατομικού μοντέλου με πληροφορία από «παρόμοιους» χρήστες. •Προσέγγιση: Αυτόματη δημιουργία κοινοτήτων χρηστών με χρήση τεχνικών ομαδοποίησης (clustering). Ατομικά μοντέλα Γενικευμένα μοντέλα Ομαδοποίηση Ενίσχυση μοντέλου

19 © September 2000 George Paliouras, All Rights Reserved19 ΜΙΤΟΣ: Τεχνική ομαδοποίησης Ομοιότητα χρηστών Freq(feat1) Freq(feat2) 01 1

20 © September 2000 George Paliouras, All Rights Reserved20 0,8 0,5 0,1 0,9 0,4 0,9 Τεχνική ομαδοποίησης Γράφος χρηστών