Incremental algorithms for effective and efficient query recommendation (Contributo in atti di convegno)

Type
Label
  • Incremental algorithms for effective and efficient query recommendation (Contributo in atti di convegno) (literal)
Anno
  • 2010-01-01T00:00:00+01:00 (literal)
Alternative label
  • Frieder O.; Broccolo D.; Nardini F. M.; Silvestri F.; Perego R. (2010)
    Incremental algorithms for effective and efficient query recommendation
    in String Processing and Information Retrieval - 17th International Symposium, SPIRE 2010
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Frieder O.; Broccolo D.; Nardini F. M.; Silvestri F.; Perego R. (literal)
Pagina inizio
  • 13 (literal)
Pagina fine
  • 24 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#titoloVolume
  • String Processing and Information Retrieval - 17th International Symposium, SPIRE 2010, Los Cabos, Mexico, October 11-13, 2010. Proceedings. Springer 2010 Lecture Notes in Computer Science ISBN 978-3-642-16320-3 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 6393 (literal)
Rivista
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#note
  • In: SPIRE 2010 - String Processing and Information Retrieval. 17th International Symposium (Los Cabos, Mexico, 11-13 October 2010). Proceedings, pp. 13 - 24. Edgar Chavez, Stefano Lonardi (eds.). (Lecture Notes in Computer Science, vol. 6393). Springer, 2010. (literal)
Note
  • ISI Web of Science (WOS) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Department of Computer Science, Georgetown University, Washington DC, USA, CNR-ISTI, Pisa (literal)
Titolo
  • Incremental algorithms for effective and efficient query recommendation (literal)
Abstract
  • Query recommender systems give users hints on possible interesting queries relative to their information needs. Most query recommenders are based on static knowledge models built on the basis of past user behaviors recorded in query logs. These models should be periodically updated, or rebuilt from scratch, to keep up with the possible variations in the interests of users. We study query recommender algorithms that generate suggestions on the basis of models that are updated continuously, each time a new query is submitted. We extend two state-of-the-art query recommendation algorithms and evaluate the effects of continuous model updates on their effectiveness and efficiency. Tests conducted on an actual query log show that contrasting model aging by continuously updating the recommendation model is a viable and effective solution. (literal)
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