Recommendation　system　has　attracted　large　amount　of　attention　in　the　field　of　E-commerce　research.　Traditional　MF　(Matrix　Factorization)　methods　take　a　global　view　on　the　user-item　rating　matrix　to　derive　latent　user　vectors　and　latent　item　vectors　for　rating　prediction.　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.　Motivated　by　this　observation,　this　paper　proposes　a　novel　rating　prediction　approach　called　RP-LGMC　(Rating　Prediction　based　on　Local　and　Global　information　with　Matrix　Clustering)　based　on　matrix　factorization　by　making　use　of　the　local　correspondence　between　user　clusters　and　item　clusters.　The　RP-LGMC　approach　consists　of　three　components.　The　first　component　is　to　partition　the　user-item　rating　matrix　into　small　blocks　by　the　sparse　subspace　clustering　(SCC)　algorithm　with　co-clustering　its　rows　(users)　and　columns　(items)　simultaneously.　The　second　component　is　local　distillation　to　extract　those　dense　and　stable　blocks　by　thresholding　block　density　and　standard　deviation.　The　third　component　is　to　predict　the　ratings　with　residual　approximation　on　the　local　blocks　and　SVD++　on　the　global　blocks　of　the　original　user-item　matrixR.　The　RP-LGMC　approach　can　not　only　reduce　the　data　sparsity　but　also　increase　the　computation　scalability.　Experiments　on　the　MovieLens-25　M　dataset　demonstrate　that　the　proposed　RP-LGMC　approach　performs　better　than　most　state-of-the-art　methods　in　terms　of　recommendation　accuracy　and　has　lower　computation　complexity　than　the　SVD++　algorithm.　(C)　2021　Published　by　Elsevier　Ltd.