Advertisement Recommendation Using Social Media

: Social media advertising is a multibillion dollar market and has become the major revenue source for Facebook and Twitter. To deliver ads to potentially interested users, these social network platforms learn a prediction model for every user based on their interests. However, as user interests often evolve slowly, the user may end up receiving repetitive advertise. We propose a context aware advertising framework it takes in account the relatively static personal interests and the dynamic news feed from friends to drive growth in the ad click-through rate. To meet the real-time requirement, we propose an online retrieval strategy that finds thousands most relevant ads matching the dynamic context when a read operation is performed. To avoid frequent retrieval when the context small, we propose a safe region method to quickly determine whether the top-k ads of a user are changed. Finally, we propose a hybrid model to integrate the merits of both methods by analyzing the dynamism news feed to determine an appropriate retrieval strategy.