The race between the rapid spread of ubiquitous computing and the Internet of Behavior has opened up a whole new avenue for the provision of personalized and context-aware services. To this end, the work presents a proactive recommendation algorithm that is meant to capitalize on IoB data to predict user behavior and deliver tailor-made content in an unobtrusive manner. With real-time behavior information added to the mix, it intends to go a step further from traditional recommendation systems. What differentiates this approach is its use and manipulation of a multitude of contextual factors: geographical context, temporal context, context of what device is being used – perhaps even emotional context as well. This live blending affords the system the ability to respond to what is happening in the real world, thus making it more reactive and relevant. The simulation results show that this awareness of context is going to generate a significantly better user engagement and accuracy of recommendation rather than traditional systems.