Recommendations for the long tail by Term-Query Graph (Abstract/Poster in atti di convegno)

Type
Label
  • Recommendations for the long tail by Term-Query Graph (Abstract/Poster in atti di convegno) (literal)
Anno
  • 2011-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1145/1963192.1963201 (literal)
Alternative label
  • Bonchi, Francesco; Perego, Raffaele; Silvestri, Fabrizio; Vahabi, Hossein; Venturini, Rossano (2011)
    Recommendations for the long tail by Term-Query Graph
    in 20th international conference companion on World Wide Web, WWW'11, Hyderabad, India, 28 March - 1 April 2011
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Bonchi, Francesco; Perego, Raffaele; Silvestri, Fabrizio; Vahabi, Hossein; Venturini, Rossano (literal)
Pagina inizio
  • 15 (literal)
Pagina fine
  • 16 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#altreInformazioni
  • Area di valutazione 01 - Scienze matematiche e informatiche ID_PUMA: /cnr.isti/2011-A6-009 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://dl.acm.org/citation.cfm?id=1963201&CFID=74367916&CFTOKEN=80133412 (literal)
Note
  • Poster (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Yahoo Research, Barcelona; CNR-ISTI, Pisa; IMT, Lucca, Italy (literal)
Titolo
  • Recommendations for the long tail by Term-Query Graph (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
  • 978-1-4503-0637-9 (literal)
Abstract
  • We define a new approach to the query recommendation problem. In particular, our main goal is to design a model enabling the generation of query suggestions also for rare and previously unseen queries. In other words we are targeting queries in the long tail. The model is based on a graph having two sets of nodes: Term nodes, and Query nodes. The graph induces a Markov chain on which a generic random walker starts from a subset of Term nodes, moves along Query nodes, and restarts (with a given probability) only from the same initial subset of Term nodes. Computing the stationary distribution of such a Markov chain is equivalent to extracting the so-called Center-piece Subgraph from the graph associated with the Markov chain itself. Given a query, we extract its terms and we set the restart subset to this term set. Therefore, we do not require a query to have been previously observed for the recommending model to be able to generate suggestions. (literal)
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