Adaptive personalization of multimodal vehicular interfaces using a hybrid recommendation approach with fuzzy preferences

摘要: User-adaptive and situation-aware presentation of information on multimodal interfaces is an important research area for coping with the increasing load driver. This paper describes a comprehensive recommendation approach inferring vague individual preferences under uncertain conditions by using fuzzy preference relations. The was applied to rank internet driver interface in vehicle. New gets successively downloaded car digital media broadcast can query (e.g. press review, cheap petrol stations, snow reports) natural language queries. Incoming elements are gradually matched ontology categories membership value based term frequency. User perferences learned from interaction recommendations content items resulting model. Three different types relations aggregated overall score: explicit preferences, implicit global preferences. later influence values one other drivers vehicular ad-hoc networks. Therefore, not solely presence kind data. Furthermore, situation modeled concept granules influences score.