In recent years, IT Service Providers have been rapidly introducing automation to their service delivery model. Driven by market pressure to reduce cost and maintain quality of services, they are looking for technologies that will allow rapid progress towards attainment of truly automated service delivery. Software monitoring systems are designed to actively collect and signal event occurrences and, when necessary, automatically generate incident tickets. Repeating events generate similar tickets, which in turn have a vast number of repeated problem resolutions likely to be found in earlier tickets. In our work, we develop techniques to recommend an appropriate resolution for incoming events by making use of similarities between the events and historical resolutions of similar events. The traditional KNN (K Nearest Neighbor) algorithm has been first applied to recommend resolutions for incoming tickets. Massive heterogeneous applications as well as various monitoring software are running on clients' servers to accomplish required tasks and to monitor system health via different metrics. It leads to generation of correlated tickets that have different symptom descriptions but similar resolutions. Furthermore, change of servers' environments can also induce similar situations in which ticket descriptions differ before and after change but could have similar resolutions. These correlated tickets cause performance degradation in ticket resolution recommendation. Therefore, we propose using SCL (structural corresponding learning) based feature adaptation to uncover feature mapping in different time intervals. Moreover, to put more insights into the periodic regularities existing in our ticket datasets, we apply our algorithm on tickets grouped by different time interval granularities. Extensive empirical evaluations on real-world ticket data sets demonstrate the effectiveness and efficiency of our proposed methods.