Traditional　matrix　factorization　(MF)　methods　take　a　global　view　on　the　user-item　rating　matrix　to　conduct　matrix　decomposition　for　rating　approximation.　However,　there　is　an　inherent　structure　in　the　user-item　rating　matrix　and　a　local　correspondence　between　user　clusters　and　item　clusters　as　the　users　induce　the　items　and　the　items　imply　the　users　in　a　recommendation　system.　This　article　proposes　a　novel　approach　called　two-stage　rating　prediction　(TS-RP)　to　matrix　clustering　with　implicit　information.　In　the　first　stage,　implicit　feedback　is　used　to　discover　the　inherent　structure　of　the　user-item　rating　matrix　by　spectral　clustering.　In　the　second　stage,　we　conduct　rating　prediction　on　the　dense　blocks　of　explicit　information　of　user-item　clusters　discovered　in　the　first　stage.　The　proposed　TS-RP　approach　can　not　only　alleviate　the　data　sparsity　problem　in　recommendation　but　also　increase　the　computation　scalability.　Experiments　on　the　MovieLens-100K　data　set　demonstrate　that　the　proposed　TS-RP　approach　performs　better　than　most　state-of-the-art　methods　of　rating　prediction　based　on　MF　in　terms　of　recommendation　accuracy　and　computation　complexity.