In this paper, the authors describe an extension to an approach previously discussed for personalization of a natural language system in the automotive domain that allows reasoning under uncertainty with incomplete preference structures. Therefore, the concept of an "information stream" is defined as an underlying model for real-time recommendation learned from previous speech queries. The stream captures contextual data based on implicit feedback from the user's speech utterances. Furthermore, a formative user study is discussed. Each study iteration has been based on a prototype that allows the user to utter natural language queries in the restaurant domain. The system responds with a ranked list of restaurant recommendations in relation to the user's context. Several driving scenarios with varying contexts have been analyzed (e.g. weekday/weekend, route destinations, traffic). Users could inspect the result lists and indicate the most preferred item. In addition to quantitative data gained from this interaction, feedback on relevance of context features and on the UI concept was collected in a post-study interview for each iteration. Based on the study findings, we outline the contextual features found to be most relevant for speech-based interaction in automotive applications. These findings will be integrated into an existing hybrid recommendation model.
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