Abstract: In described embodiments, fluid user models are employed for personalized mobile applications, referred to herein as recommender systems. The described embodiments augment media with additional information highly personalized to a user based on a variety of factors with different time sensitivity and include, but are not limited to, a user's general preferences, history, immediate context and attention, and emotional states when the user experiences and/or interacts with the media. Embodiments solve various problems of information overload stemming from both i) the complexity in characterizing the information and ii) its potential relevancy and in understanding and coding the user's needs.

Abstract: In described embodiments, white space channel recommendations are provided to a device in a white space channel allocation environment when the device is either stationary or mobile. The channel recommendation comprises of a prioritized channel list where available channels are ordered as a function of their relative optimal applicability. Once a basic list of white space channels is generated based on simple white space channel availability, a final, recommended and prioritized list is computed by a variety of cooperating optimization modules that take into account other objective and subjective parameters related to the user and device. Such other parameters might include projected usage of the spectrum, projected usage duration, device current and projected location and terrain, a user of the device's plans/calendar, and other projections based on user or device history.