Efficient　interest　prediction　for　social　networks　is　critical　for　both　users　and　service　providers　for　behavior　analysis　and　a　series　of　extension　services.　However,　most　existing　approaches　are　inefficient,　incomplete　or　isolated.　In　this　paper,　we　propose　combination　of　Gaussian　and　Markov　approaches　(namely,　GAM)　as　typical　soft　computing　technology　for　interest　prediction　of　social　intelligent　multimedia　systems.　GAM　model　considers　＂the　number　of　posted　messages＂　as　the　only　parameter,　and　defines　selection　logic　to　implement　either　Gaussian　or　Markov　based　approaches.　Our　proposed　solution　takes　the　advantage　of　Gaussian　model　in　prediction　accuracy　and　computation　complexity,　and　advantage　of　Markov　model　in　high　availability.　Further　experiments　illustrate　that　our　solution　achieves　higher　prediction　accuracy　of　94.3%　(without　considering　the　influence　of　swing　users),　with　the　best　result　achieved　ever.