Dynamically Adaptive User Profiling for Personalized Recommendations

Modelling user interests for time sensitive domains like RSS feeds and spontaneous social media has been a vibrant research activity in recent times. Although numerous efforts have been invested in to the personalisation of dynamic web content, the voluminous and diversified production of continuous online information still poses significant research challenges. In this paper, we propose a profiling mechanism that learns the user access patterns in a dynamic environment like RSS feeds. Main goal of the proposed mechanism is the retrieval optimisation and personalised recommendation of RSS feeds in close to real time. The mechanism allocates personalized time windows based on the learned access patterns and tries to minimize the chances of time-sensitive information from being missed by the user using a delay minimisation algorithm based on Non-homogenous Poisson Process. The mechanism acquires implicit feedback from the user interaction to calculate potential recommendations. The experiments conducted prove the significance of the mechanism in terms of optimised information retrieval and faster adaptation process where it clearly outperforms the other mechanisms in the literature on these properties.