18.1753, Diss: Computational Ling: Stegmann: 'LINGUINI - Acquiring Individua...'

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LINGUIST List: Vol-18-1753. Fri Jun 08 2007. ISSN: 1068 - 4875.

Subject: 18.1753, Diss: Computational Ling: Stegmann: 'LINGUINI - Acquiring Individua...'

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1)
Date: 08-Jun-2007
From: Rosmary Stegmann < rs at r-stegmann.de >
Subject: LINGUINI - Acquiring Individual Interest Profiles by Means of Adaptive Natural Language Dialog

 

	
-------------------------Message 1 ---------------------------------- 
Date: Fri, 08 Jun 2007 15:35:55
From: Rosmary Stegmann < rs at r-stegmann.de >
Subject: LINGUINI - Acquiring Individual Interest Profiles by Means of Adaptive Natural Language Dialog 
 


Institution: Technical University of Munich 
Program: no specific program name 
Dissertation Status: Completed 
Degree Date: 2006 

Author: Rosmary Stegmann

Dissertation Title: LINGUINI - Acquiring Individual Interest Profiles by Means
of Adaptive Natural Language Dialog 

Linguistic Field(s): Computational Linguistics


Dissertation Director(s):
Manfred Pinkal
Johann Schlichter

Dissertation Abstract:

User information is needed by adaptive systems in order to tailor
information and product offers to the needs and preferences of individual
users. Personalized Recommender Systems are adaptive systems that
automatically generate recommendations on the basis of individual user
profiles. Most existing Recommender Systems, however, are based on rather
simple and mainly standardized profile information, which often delimits
the adequacy of the recommendations they generate for an individual user.
More adequate recommendations could be generated on the basis of more
individual and representative user profiles that also integrate complex
information, for example about personal interests or lifestyle.

Furthermore, most adaptive systems acquire profile information only for
their own purposes and do not allow for an exchange of this information
with other applications the user wants to use. Above all, existing explicit
profiling methods suffer from severe drawbacks which limit their
utilizability in practice. Especially for mobile scenarios, in which a
spoken language interaction with the user is required, no suitable explicit
profiling methods exist as yet that integrate a solution for all of the
above mentioned problems.

This thesis presents a solution for acquiring detailed information about
personal interests of users by means of an adaptive natural language
dialog. We have developed a comprehensive explicit profiling framework,
LINGUINI, which integrates a dialog management and profile management
approach. Because of the natural language processing methods applied, this
profiling approach is especially suitable for situations in which spoken
language is required (e.g. in a vehicle), but it is also applicable with a
user interface for typed input and output (e.g. for Internet and E-Commerce
platforms). The acquired information can be used by various types of
adaptive systems for which user interests are relevant.

During our profiling dialog, users are able to formulate their interests in
their own words. The dialog adapts to each user individually and is able to
find and talk about new interests related to the interests already
mentioned by the user. The dialog management approach integrates a
sociological target group model that clusters users into groups according
to their interests. The groups do not serve as user profiles, however, but
are used for providing clues about suitable next questions or related
topics. With this adaptive approach, we are able to create truly
personalized profiles that are different for each user in contents and
structure. By employing the lexical-semantic network GermaNet, our
profiling approach allows for representing interests in a semantically
structured way and for interpreting and storing new user information
dynamically that has not been predefined in the user model before.

We implemented our adaptive profiling approach as a comprehensive prototype
system and evaluated it by means of a user study which investigates user
acceptance, dialog adaptability, and profile quality. The study shows that
users, in fact, appreciate the adaptive capabilities of the profiling
system. The users' willingness to apply the system is high and they
consider this approach very suitable for a variety of mobile and non-mobile
situations and adaptive applications. 





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