Recommendation system has been seen to be very useful for user to select an item amongst many. Most existing recommendation systems rely either on a collaborative approach or a content-based approach to make recommendations. We have applied machine learning techniques to build recommender systems. We have taken two approaches. In the first approach a content-based recommender system is built, which uses collaborative data, so that it gets the effect of a hybrid approach to get better result of recommendation. Attributes used for content-based recommendations are assigned weights depending on their importance to users. The weight values are estimated from a set of linear regression equations obtained from a social network graph which captures human judgment about similarity of items. In the second approach agent of call center have been recommended some procedure depending on the current state of online call. A combination of K-Means Algorithm and Hidden Markov model is used