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Description
  • Web search heuristics assume we have the user profile in the form of particular attribute ordering and a fuzzy aggregation function representing the user combining function. Having this there are sufficient algorithms for searching top-k answers. Although we have already obtained significant progress in mining user combining function, there is still a problem of sample creation for user evaluation. Problem of finding particular attribute user ordering still remains a problem, too. We overview our former approach, lessons learned and describe analysis and proposal of an upgrade of our system. Our main contributions are the description of an iterative process of acquisition of user preferences and proposal of two methods for creating a sample set for user evaluation during each iteration.
  • Web search heuristics assume we have the user profile in the form of particular attribute ordering and a fuzzy aggregation function representing the user combining function. Having this there are sufficient algorithms for searching top-k answers. Although we have already obtained significant progress in mining user combining function, there is still a problem of sample creation for user evaluation. Problem of finding particular attribute user ordering still remains a problem, too. We overview our former approach, lessons learned and describe analysis and proposal of an upgrade of our system. Our main contributions are the description of an iterative process of acquisition of user preferences and proposal of two methods for creating a sample set for user evaluation during each iteration. (en)
Title
  • PHASES: A User Profile Learning Approach for Web Search
  • PHASES: A User Profile Learning Approach for Web Search (en)
skos:prefLabel
  • PHASES: A User Profile Learning Approach for Web Search
  • PHASES: A User Profile Learning Approach for Web Search (en)
skos:notation
  • RIV/00216208:11320/07:10084056!RIV11-MSM-11320___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(1ET100300419), P(1ET100300517), V, Z(MSM0021620838)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 441173
http://linked.open...ai/riv/idVysledku
  • RIV/00216208:11320/07:10084056
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • user preference; inductive procedures; sample set; Web user profile (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [13CDB05C0735]
http://linked.open...v/mistoKonaniAkce
  • Fremont, USA
http://linked.open...i/riv/mistoVydani
  • LOS ALAMITOS
http://linked.open...i/riv/nazevZdroje
  • 2007 IEEE/WIC/ACM INTERNATIONAL JOINT CONFERENCES ON WEB INTELLIGENCE (WI) AND INTELLIGENT AGENT TECHNOLOGIES (IAT)
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Vojtáš, Peter
  • Eckhardt, Alan
  • Horváth, T.
http://linked.open...vavai/riv/typAkce
http://linked.open...ain/vavai/riv/wos
  • 000253307900131
http://linked.open.../riv/zahajeniAkce
http://linked.open...n/vavai/riv/zamer
number of pages
http://purl.org/ne...btex#hasPublisher
  • IEEE Computer Society
https://schema.org/isbn
  • 978-0-7695-3026-0
http://localhost/t...ganizacniJednotka
  • 11320
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