Bibtex de la publication

@InProceedings{ DaTaDiBo2009.1,
author = {Daoud, Mariam and Tamine, Lynda and Dinh, Duy and Boughanem, Mohand},
title = "{Contextual Query Classification For Personalizing Informational Search (regular paper)}",
booktitle = "{Web Information Systems Engineering, kerkennah Island, Sfax, Tunisia, 12/06/2009-14/06/2009}",
year = {2009},
month = {juin},
publisher = {ACM},
address = {},
pages = {(electronic medium)},
language = {anglais},
keywords = {user intent, query profile, user profile, contextual query classification, personalized search},
abstract = {It is widely assumed that exploiting multiple sources of evidence allows improving information retrieval approaches. Personalized information retrieval aims to better fit the user information needs by integrating the user profile elements like interests, preferences and tasks in the information retrieval process. Recent studies exploit evidence like the user intent behind the query, classified as informational, navigational or transactional in order to carry out a specific search. However, the strategies involved focus solely on exploiting document features to leverage the relevance estimation according to the user intent category. In this paper, we show how to incorporate both user intent and user profile evidences, in the same framework, for personalizing the informational search. Our framework description includes user intent prediction based on contextual query classification method, and user profiling based on modeling the user interests. Query classification relies on using both query features and quality indicators of the current query session category called the query profile. We define a query session as a sequence of queries of the same type. Then, personalizing informational search emphasizes the user profile in a personalized document ranking based on combining contextual score and original score of the document. Preliminary experimental results carried out using TREC data show that our approach is promising.}