In this paper, we generalize the link prediction problem to an interaction prediction problem. Compared with links in social networks, interactions can occur several times repeatedly. Based on the observation, we formulate an event-triggered interaction prediction problem. For example, we may want to know when a user connects to a website (e.g. Facebook), who will also connect to the website. We propose a Profile-based Interaction Prediction Framework (PIPF) which can solve the event-triggered interaction prediction problem efficiently and effectively. In PIPF, we first transform the interaction log into a Sliding-window Evolving Graph (SEG) to reduce the data volume and incrementally update SEG as interaction log grows. Then, we build profiles designed to present users' behavior by extracting the static and surprising features from SEG. The static (respectively, surprising) feature reflects the regularity of users' behavior (respectively, the temporal behavior). When an event occurs, we compute the similarity between the event and each candidate link. We propose two similarity functions for static and surprising features and an automatic selection strategy to control the influence of the two features. We use a real dataset that records Internet connections to evaluate the scalability, efficiency, and effectiveness of PIPF. The experimental results show that PIPF is far more scalable and efficient than the previous methods to perform real-time prediction.