| Abstract |
Users access large amounts of information resources (documents or data) mainly through search functions,
where they type a few words and the system (web search engine, query engine) returns a linear
list of hits. While this is often satisfactory for focalized search, it does not provide enough support for
recall-oriented (exploratory) information needs, which constitute the majority according to various user
studies.
The interaction of Faceted and Dynamic Taxonomies (FDT), is a highly prevalent model for exploratory
search, which allows users to get an overview of the information space (e.g. search results) and
offer them various groupings of the results (based on their attributes, metadata, or other dynamically
mined information). These groupings enable users to restrict their focus gradually and in a simple way
(through clicks, i.e. without having to formulate queries), enabling them to locate resources that would
be difficult to locate otherwise (especially the low ranked ones).
The enrichment of search mechanisms with preferences could be proved useful for recall-oriented
information needs. However, the current approaches for preference-based access (mainly from the area
of databases), seem to ignore the fact that users should be acquainted with the information space and
the available choices for describing effectively their preferences.
In this dissertation we extend the interaction model of FDT with preference actions that allow users
to express their preferences interactively, gradually, and in a simple way.
Initially, we introduce a preference framework appropriate for information spaces comprising resources
described by attributes whose values can be hierarchically valued and/or multi-valued. We
define the language, its semantics and the required algorithms. The framework supports preference
inheritance in the hierarchies, automatic conflict resolution, as well as preference composition (prioritization,
Pareto and their combination).
Subsequently, we enrich the FDT model with preference actions and we propose logical optimizations
and methods for exploiting the intrinsic characteristics of the FDT-based interaction, aiming at making it applicable to large amounts of information. Then, we present the design and the implementation
of the web-based system Hippalus, which realizes the extended interaction model.
Regarding user benefits, at first we theoretically analyze user gain in terms of the number and difficulty
of choices, and then we describe and analyze three user-based evaluations that we have conducted.
The first investigates the degree of effectiveness of preferences (and the effort to express them) when
users are not aware of the available choices. The results showed that only 20% of the users managed to
express effective preferences without knowing the available choices.
The second comparatively evaluates FDT and other exploratory models. The results showed that the
majority of users preferred FDT, was more satisfied by FDT and achieved higher rates of task completion
with FDT.
The last one concerns the evaluation of the preference-enriched FDT as realized by Hippalus. The
results were impressive. Even in a very small dataset, with the preference-enriched FDT all users successfully
completed all tasks in 1/3 of the time and with 1/3 of the actions in comparison to the plain FDT.
Moreover all (100%) of the users (either plain or experts) preferred the preference-enriched interface.
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