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PNS: Personalized Multi-Source News Delivery

PNS: Personalized Multi-Source News Delivery. Georgios Paliouras(1), Mouzakidis Alexandros (1), Christos Ntoutsis(2), Angelos Alexopoulos(3), Christos Skourlas(2) (1) Institute of Informatics and Telecommunications, NCSR "Demokritos", Greece {paliourg, alexm }@iit.demokritos.gr

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PNS: Personalized Multi-Source News Delivery

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  1. PNS: Personalized Multi-Source News Delivery Georgios Paliouras(1), Mouzakidis Alexandros(1), Christos Ntoutsis(2), Angelos Alexopoulos(3), Christos Skourlas(2) (1) Institute of Informatics and Telecommunications, NCSR "Demokritos", Greece {paliourg, alexm}@iit.demokritos.gr (2) Dept. of informatics ,Technological Institute of Athens, Greece (3) Dept. of Informatics and Telecommunications, University of Athens, Greece

  2. Outline • Content aggregation News source aggregation • Information extraction Url extraction News content extraction • Personalization Single user modeling User communities, stereotypes and feature groups • Summary and future work

  3. The PNS approach • News source aggregation • Data collection (spiders and extraction) • User modeling • Presentation Filtering

  4. Users Webbrowser Content Server Content Database 1 2 3 Web Web Architecture News Content Scanner http Request – Response News Content Presenter News Content Selector http Request – Response News Sources Personalization Server

  5. News Content Scanner • Html and RSS Feed parsing • Extraction of urls that contain articles • News content extraction from URLs

  6. News Content Scanner Architecture of News Content Scanner

  7. Personalization Server (PServer) A general purpose personalization web server constructs and maintains models for • individual users • stereotypes • user communities • feature groups.

  8. PServer • Independent of particular applications • Able to adapt to application parameters. • Covers a wide range of services and functionality. • Easily expandable. • Easy to set up and use by ongoing projects

  9. PServer • Personalization functionality as a web service: • Centrally installed and maintained. • Completely separated from applications. • Possible for many applications to use concurrently. • Easily accessible through HTTP. • Towards an adaptable model. • central concepts: application features, users. • Feature semantics not part of PServer. • Features organization in a tree-like manner facilitates querying. • lang.english lang.greek page10.top_frame.banner8

  10. PServer • Application communicates with PServer through HTTP requests. • PServer response body is formatted in XML. • Especially developed XSLs allow any browser to suitably display the PServer response. • Three distinct modes of operation: personal, stereotype.community • Application requests provide full control for inserting, updating, deleting and querying data: • Each request type can take up many forms, representing a set of possible actual requests.

  11. External databases External databases Personalization parameters Content database User models Knowledge base External databases Thin-client layer Pserver: Application Usage Personalization layer Data import Inference engine Web usage mining Knowledge editor Personalization meta-service designer User model management Personalization interface Content management Application layer Application designer User interface user

  12. News Content Selector • Structures the content that must be presented based on the request • Personalize the content

  13. News Content Presenter • Registration of new users. • Identification of registered users. • Presentation of the daily news that exist in the Content Index Database according to the user’s preferences (personal e-paper). • Presentation of the daily news according to the preferences of users that belong to the same stereotype. • Presentation of the daily news according to the preferences of users that belong to the same user community.

  14. News Content Presenter • Presentation of the daily news that belong to the same news itemset as the currently viewed item. • Personalized presentation of the news of previous days. • Text-based search and presentation of news titles using keywords. • Ability to retrieve news highlight in specific dates from the database.

  15. News Content Presenter PNS User interface

  16. Summary • Easy to install • 100% configurable • A wide range of RDBMS can be used • 100% Platform Independent • PNS now contains 12 news source and 10 categories • Static wrapper maintenance

  17. Future work • More sources will be added in the future • A new module for dynamic wrapper maintenance have to be build • Content based personalization will be supported • Pserver will be published online as open source software

  18. Thank you for your concentration User Modeling 2007 11th International Conference on User Modeling (UM 2007) Corfu , Greece , 25-29 June, 2007 Organized by the National Center for Scientific Research "Demokritos", in collaboration with the Ionian University and User Modeling Inc. http://www.iit.demokritos.gr/um2007/

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