528 P. Warnestal Different forms of adaptivity are prominent in these phases. In each phase there is thus a potential for modeling user attributes and or preferences. For example, individual users have their own way of expressing queries and their vocabulary preferences. This is clearly an adaptive functionality of the interpre. ation phase, and is facilitated by a personalized lexicon and grammar. Moving to the dialogue management phase, it is considered necessary to include adap- tation if the dialogue is to be viewed as cooperative 7. One important example of adaptation of the dialogue is the DP's strategy and initiative, such as whether the DP should be more pro-active or more reactive. When considering domain reasoning-and connection to the system back-end--there are yet other types of user data that require modeling(e.g. item ratings for collaborative filtering systems, or user needs and interests for knowledge-based recommender engines etc. ) Finally, Zukerman and Litman [10 conclude that user models are required in order to enable systems to generate appropriate and relevant responses in logue systems. The generation phase is concerned with generating responses that fit a specific user(e.g. content planning, surface generation, and modalit considerations and feedback) User-adaptive system performance depends on how the user model is (1) initialized,(2)updated, and ( 3) put to use in order to achieve adaptive func- tionality. These three aspects need to be addressed for each phase's user model. The aspects and phases define a two-dimensional problem space of modularized user modeling components that frames this research My work is aimed at investigating what kind of modeling is carried out at the different points of this problem space; and finding out how different phenomena and problems are handled in each phase Contributions for developers include a theoretical framework and corresponding tool linking what needs to be modeled and how, with the desired adaptive functionality of the system 3 Previous and Current Work So far, I have developed a conversational movie recommender system 5, which implements an empirically based recommendation dialogue strategy described in 9. The system supports initialization and continuous updating of a user' movie preferences, and gives personalized recommendations and explanations through nl dialogue. In terms of the architecture outlined above this work thus focuses on the domain reasoning phase, and forms the base on which I will continue to develop and investigate user modeling for the remaining phases Using an existing phase-based dialogue system architecture 3. I have started to work on a user modeling component framework that functions as pluggable modularized intercepting filters for each of the standard dialogue system phases as outlined above. The filters are configurable and will contain formalisms and mechanisms for initializing, updating, and putting the different models to use.528 P. W¨arnest˚al Different forms of adaptivity are prominent in these phases. In each phase there is thus a potential for modeling user attributes and/or preferences. For example, individual users have their own way of expressing queries and their vocabulary preferences. This is clearly an adaptive functionality of the interpre￾tation phase, and is facilitated by a personalized lexicon and grammar. Moving to the dialogue management phase, it is considered necessary to include adap￾tation if the dialogue is to be viewed as cooperative [7]. One important example of adaptation of the dialogue is the dp’s strategy and initiative, such as whether the dp should be more pro-active or more reactive. When considering domain reasoning—and connection to the system back-end—there are yet other types of user data that require modeling (e.g. item ratings for collaborative filtering systems, or user needs and interests for knowledge-based recommender engines, etc.). Finally, Zukerman and Litman [10] conclude that user models are required in order to enable systems to generate appropriate and relevant responses in dialogue systems. The generation phase is concerned with generating responses that fit a specific user (e.g. content planning, surface generation, and modality considerations and feedback). User-adaptive system performance depends on how the user model is (1) initialized, (2) updated, and (3) put to use in order to achieve adaptive func￾tionality. These three aspects need to be addressed for each phase’s user model. The aspects and phases define a two-dimensional problem space of modularized user modeling components that frames this research. My work is aimed at investigating what kind of modeling is carried out at the different points of this problem space; and finding out how different phenomena and problems are handled in each phase. Contributions for developers include a theoretical framework and corresponding tool linking what needs to be modeled, and how, with the desired adaptive functionality of the system. 3 Previous and Current Work So far, I have developed a conversational movie recommender system [5], which implements an empirically based recommendation dialogue strategy described in [9]. The system supports initialization and continuous updating of a user’s movie preferences, and gives personalized recommendations and explanations through nl dialogue. In terms of the architecture outlined above this work thus focuses on the domain reasoning phase, and forms the base on which I will continue to develop and investigate user modeling for the remaining phases. Using an existing phase-based dialogue system architecture [3], I have started to work on a user modeling component framework that functions as pluggable modularized intercepting filters for each of the standard dialogue system phases as outlined above. The filters are configurable and will contain formalisms and mechanisms for initializing, updating, and putting the different models to use