With　the　rapid　development　of　Internet　and　Web　techniques,　Cross-Domain　Recommendation　(CDR)　models　have　been　widely　explored　for　resolving　the　data-sparsity　and　cold-start　problem.　Meanwhile,　most　CDR　models　should　utilize　explicit　domain-shareable　information　(e.g.,　overlapped　users　or　items)　for　knowledge　transfer　across　domains.　However,　this　assumption　may　not　be　always　satisfied　since　users　and　items　are　always　non-overlapped　in　real　practice.　The　performance　of　many　previous　works　will　be　severely　impaired　when　these　domain-shareable　information　are　not　available.　To　address　the　aforementioned　issues,　we　propose　the　Joint　Preference　Exploration　and　Dynamic　Embedding　Transportation　model　(JPEDET)　in　this　paper　which　is　a　novel　framework　for　solving　the　CDR　problem　when　users　and　items　are　non-overlapped.　JPEDET　includes　two　main　modules,　i.e.,　joint　preference　exploration　module　and　dynamic　embedding　transportation　module.　The　joint　preference　exploration　module　aims　to　fuse　rating　and　review　information　for　modelling　user　preferences.　The　dynamic　embedding　transportation　module　is　set　to　share　knowledge　via　neural　ordinary　equations　for　dual　transformation　across　domains.　Moreover,　we　innovatively　propose　the　dynamic　transport　flow　equipped　with　linear　interpolation　guidance　on　barycentric　Wasserstein　path　for　achieving　accurate　and　bidirectional　transformation.　Our　empirical　study　on　Amazon　datasets　demonstrates　that　JPEDET　outperforms　the　state-of-the-art　models　under　the　CDR　setting.　Copyright　©　2024,　Association　for　the　Advancement　of　Artificial　Intelligence　(www.aaai.org).　All　rights　reserved.