Social media is an emerging trend for communication between people worldwide and it has attracted millions of users. A typical characteristic of such sites is that they allow anyone to post or like anything they like on any subject. Here the user’s feedback and satisfaction is playing a vital role. In some cases the user may not give the feedback or reviews which they have viewed or downloaded directly. Instead of that the user is just searching the available things based on their interest. So there is no possibility for capturing the domain interest / behavior of the user explicitly. We investigate the problem of predicting user’s domain interest in social Medias, where we attempt to predict whether a user will be satisfied with domain suggestion explicitly. The domains are may be sports, motivation, nature, positive quotes, cooking, healthcare, gardening, general knowledge, job recruitment, pencil arts, shopping, comedies, books, school memory, data mining, networks, cloud computing, grid computing, image processing, communication networks, mobile computing, mechanical engineering. We find the maximum number of like in which domain. And we recommend events based on that domain. We complement our results with a thorough investigation of the interactions and information patterns in their likes and shares that correlate with domain interest of a user.