Adaptive query expansion (QE) allows users to better define their search domain by supplementing the original query with additional terms related to their preferences and information needs. The system we present is an extension of the traditional QE techniques, which rely on the computation of two-dimensional co-occurrence matrices. Our system makes use of three-dimensional co-occurrence matrices, where the added dimension is represented by semantic classes (i.e., categories comprising all the terms that share a semantic property) related to the folksonomy extracted from social book-marking services such as delicious, Digg, and StumbleUpon. The generation of the user profile occurs through the creation of a model that is dynamically updated using the information gleaned from the searches (visited pages and corresponding search queries). The system analyzes the input queries and, if they actually reflect the interests already shown by the user in previous searches, it returns different QEs involving different semantic fields. The output of the system is structured in different blocks categorized through keywords, thus helping the user judge which result is most relevant to him. The results of an experimental evaluation involving real users are reported.
|Titolo:||Folksonomy-based adaptive query expansion|
|Data di pubblicazione:||2012|
|Citazione:||Biancalana, C., Gasparetti, F., Micarelli, A., Miola, A., & Sansonetti, G. (2012). Folksonomy-based adaptive query expansion. In CEUR Workshop Proceedings.|
|Appare nelle tipologie:||4.1 Contributo in Atti di convegno|