User　cold-start　recommendation　aims　to　provide　accurate　items　for　the　newly　joint　users　and　is　a　hot　and　challenging　problem.　Nowadays　as　people　participant　in　different　domains,　how　to　recommend　items　in　the　new　domain　for　users　in　an　old　domain　has　become　more　urgent.　In　this　paper,　we　focus　on　the　Dual　Cold-Start　Cross　Domain　Recommendation　(Dual-CSCDR)　problem.　That　is,　providing　the　most　relevant　items　for　new　users　on　the　source　and　target　domains.　The　prime　task　in　Dual-CSCDR　is　to　properly　model　user-item　rating　interactions　and　map　user　expressive　embeddings　across　domains.　However,　previous　approaches　cannot　solve　Dual-CSCDR　well,　since　they　separate　the　collaborative　filtering　and　distribution　mapping　process,　leading　to　the　error　superimposition　issue.　Moreover,　most　of　these　methods　fail　to　fully　exploit　the　cross-domain　relationship　among　large　number　of　non-overlapped　users,　which　strongly　limits　their　performance.　To　fill　this　gap,　we　propose　User　Distribution　Mapping　model　with　Collaborative　Filtering　(UDMCF),　a　novel　end-to-end　cold-start　cross-domain　recommendation　framework　for　the　Dual-CSCDR　problem.　UDMCF　includes　two　main　modules,　i.e.,　rating　prediction　module　and　distribution　alignment　module.　The　former　module　adopts　one-hot　ID　vectors　and　multi-hot　historical　ratings　for　collaborative　filtering　via　a　contrastive　loss.　The　latter　module　contains　overlapped　user　embedding　alignment　and　general　user　subgroup　distribution　alignment.　Specifically,　we　innovatively　propose　unbalance　distribution　optimal　transport　with　typical　subgroup　discovering　algorithm　to　map　the　whole　user　distributions.　Our　empirical　study　on　several　datasets　demonstrates　that　UDMCF　significantly　outperforms　the　state-of-the-art　models　under　the　Dual-CSCDR　setting.　©　2024　ACM.