In recent years, IT Service Providers have been rapidly transforming to an automated service delivery model. This is due to advances in technology and driven by the unrelenting market pressure to reduce cost and maintain quality. Tremendous progress has been made to date towards attainment of truly automated service delivery; that is, the ability to deliver the same service automatically using the same process with the same quality. However, automating Incident and Problem Management continuous to be a difficult problem, particularly due to the growing complexity of IT environments. Software monitoring systems are designed to actively collect and signal event occurrances 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 this paper we find an appropriate resolution by making use of similarities between the events and previous resolutions of similar events. Traditional KNN (K Nearest Neighbor) algorithm has been used to recommend resolutions for incoming tickets. However, the effectiveness of recommendation heavily relies on the underlying similarity measure in KNN. In this paper, we significantly improve the similarity measure used in KNN by utilizing both the event and resolution information in historical tickets via a topic-level feature extraction using the LDA (Latent Dirichlet Allocation) model. In addition, when resolution categories are available, we propose to learn a more effective similarity measure using metric learning. Extensive empirical evaluations on three ticket data sets demonstrate the effectiveness and efficiency of our proposed methods.