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This thesis addresses a problem related to usage analysis in information retrieval
systems. Indeed, we exploit the history of search queries as support of analysis to
extract a profile model. The objective is to characterize the user and the data source
that interact in a system to allow different types of comparison (user-to-user, sourceto-
source, user-to-source). According to the study we conducted on the work done on
profile model, we concluded that the large majority of the contributions are strongly
related to the applications within they are proposed. As a result, the proposed
profile models are not reusable and suffer from several weaknesses. For instance,
these models do not consider the data source, they lack of semantic mechanisms and
they do not deal with scalability (in terms of complexity). Therefore, we propose
a generic model of user and data source profiles. The characteristics of this model
are the following. First, it is generic, being able to represent both the user and the
data source. Second, it enables to construct the profiles in an implicit way based on histories of search queries. Third, it defines the profile as a set of topics of interest,
each topic corresponding to a semantic cluster of keywords extracted by a specific
clustering algorithm. Finally, the profile is represented according to the vector space
model. The model is composed of several components organized in the form of a
framework, in which we assessed the complexity of each component.
The main components of the framework are:
• a method for keyword queries disambiguation
• a method for semantically representing search query logs in the form of a
taxonomy;
• a clustering algorithm that allows fast and efficient identification of topics of
interest as semantic clusters of keywords;
• a method to identify user and data source profiles according to the generic
model.
This framework enables in particular to perform various tasks related to usage-based
structuration of a distributed environment. As an example of application, the framework
is used to the discovery of user communities, and the categorization of data
sources. To validate the proposed framework, we conduct a series of experiments
on real logs from the search engine AOL search, which demonstrate the efficiency
of the disambiguation method in short queries, and show the relation between the
quality based clustering and the structure based clustering.