LINGUINI - 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. 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.