Query suggestions using query-flow graphs

  • Authors:

  • Paolo Boldi;Francesco Bonchi;Carlos Castillo;Debora Donato;Sebastiano Vigna

  • Affiliations:

  • Università degli, Studi di Milano, Italy;Yahoo! Research Labs, Barcelona, Spain;Yahoo! Research Labs, Barcelona, Spain;Yahoo! Research Labs, Barcelona, Spain;Università degli, Studi di Milano, Italy

  • Venue:
  • Proceedings of the 2009 workshop on Web Search Click Data
  • Year:
  • 2009

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Abstract

The query-flow graph [Boldi et al., CIKM 2008] is an aggregated representation of the latent querying behavior contained in a query log. Intuitively, in the query-flow graph a directed edge from query qi to query qj means that the two queries are likely to be part of the same search mission. Any path over the query-flow graph may be seen as a possible search task, whose likelihood is given by the strength of the edges along the path. An edge (qi, qj) is also labelled with some information: e.g., the probability that user moves from qi to qj, or the type of the transition, for instance, the fact that qj is a specialization of qi. In this paper we propose, and experimentally study, query recommendations based on short random walks on the query-flow graph. Our experiments show that these methods can match in precision, and often improve, recommendations based on query-click graphs, without using users' clicks. Our experiments also show that it is important to consider transition-type labels on edges for having good quality recommendations. Finally, one feature that we had in mind while devising our methods was that of providing diverse sets of recommendations: the experimentation that we conducted provides encouraging results in this sense.