The query-flow graph 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.
Query suggestions using query-flow graphs / P. Boldi, F. Bonchi, C. Castillo, D. Donato, S. Vigna - In: WSDM 2009 : second ACM international conference on Web search and data mining, Barcelona, Spain, February 9-12, 2009New York : ACM Press, 2009. - ISBN 9781605584348. - pp. 56-63 (( Intervento presentato al 2. convegno ACM International Conference on Web Search and Data Mining tenutosi a Barcelona, Spain nel 2009 [10.1145/1507509.1507518].
Query suggestions using query-flow graphs
P. BoldiPrimo
;S. VignaUltimo
2009
Abstract
The query-flow graph 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.Pubblicazioni consigliate
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