Rosy Stegmann
Acquiring Individual Interest Profiles by Means of Adaptive Natural Language Dialog


User information is needed by adaptive systems in order to tailor
information and product offers to the needs and preferences of
individual users. Most adaptive systems such as 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. In this talk, we present a
solution for acquiring such user information by means of an adaptive
natural language dialog. Our explicit profiling framework integrates a
dialog management and profile management approach and 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
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. 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.