Personalized　recommendation　systems　play　a　crucial　role　in　alleviating　information　overload　and　satisfying　users＇　speci¯c　preferences.　To　address　the　challenges　of　inadequate　user　historical　data　extraction　and　the　cold　start　problem　inherent　in　traditional　movie　recommendation　systems,　we　present　a　novel　personalized　movie　recommendation　model　known　as　\movie　recommendation　with　starring　roles　and　ratings＂　(MSR).　By　incorporating　a　multi-head　attention　mechanism,　the　model　captures　intricate　relationships　among　diverse　data　¯elds　within　users＇　viewing　records　and　facilitates　the　extraction　of　user　features　through　the　basic　information-rating　joint　attention　network　(BRJA).　The　gate　mechanism　e±ciently　integrates　fundamental　movie　information　and　average　score　into　the　movie　representation　vector,　thereby　generating　candidate　movie　features.　MSR　can　e®ectively　provide　recommendations　even　when　confronted　with　limited　user　information,　e®ectively　mitigating　the　cold　start　problem.　Comparative　experiments　on　the　movie　lens　dataset　and　ablation　experiments　focusing　on　key　modules　demonstrate　the　e®ectiveness　of　MSR　in　improving　movie　recommendations.　©　World　Scientific　Publishing　Europe　Ltd.