<i>NActSeer</i>

Nowadays social network platforms like Twitter, Facebook, Weibo have created a new landscape to communicate with our friends and the world at large. In this landscape our social activities, purchase decisions, check-ins etc. become available immediately to our friends/followers and thus encouraging them to involve in the same activity. This gives rise to the question, given a user and her friends' previous actions, can we predict what is she going to do next? This problem can serve as a good indicator enabling policy research, targeted advertising, assortment planning etc. To capture such sequential mechanism two broad classes of methods have been proposed in the past. First one is the Markov Chain (MC), which assumes user's next action can be predicted based on her most recently taken actions while the second type of approach i.e. Recurrent Neural Network (RNN) tries to model both long and short term preferences of a user. However, none of the two classes of models contain any integrated mechanism to capture the preferences of neighbor's actions. To fill this gap, we propose a social network augmented neural network model named NActSeer which takes the neighbors' actions into account in addition to the user's history. To achieve this NActSeer maintains a dynamic user embedding based on the activities within a time window. It then learns a feature representation for each user which is augmented by her neighbors. Empirical studies on four real-world datasets show that NActSeer is able to outperform several classical and state-of-the-art models proposed for similar problems and achieves up to 71% performance boost.