Destination　prediction　based　on　the　partial　trajectory　of　a　moving　vehicle　is　vital　for　urban　mobility　applications.　Recent　research　efforts　focus　on　improving　the　prediction　accuracy　by　incorporating　more　spatio-temporal　semantics　through　complex　model　architectures,　which　inevitably　impact　the　generalization　and　scalability　due　to　ad-hoc　hyper-parameters　and　heavier　computations.　In　the　present　study,　we　propose　a　novel　Location　Recommendation　System　for　Destination　Prediction,　LRS4DP.　Through　an　integrated　design　of　several　technologies　(map-matching,　deep　learning　and　recommender　system),　LRS4DP　provides　an　end-to-end　solution　for　destination　prediction　based　on　input　trajectories　and　road　network　configurations.　By　adopting　a　node-based　spatial　discretization　scheme　through　map-matching,　LRS4DP　is　able　to　adapt　according　to　the　local　road　network　density　and　generalize　to　different　urban　layouts.　As　compared　to　the　state-of-the-art　algorithms,　our　proposed　Top-K　formulation　based　on　individual　road　nodes　leads　to　fundamentally　better　spatial　precision　and　prediction　accuracy　even　with　simple　model　architectures.　We　further　designed　the　offline　training　and　online　serving　as　a　location　recommendation　system　to　achieve　better　scalability　and　flexible　trade-off　between　performance　and　run-time.　The　experimental　evaluation　of　two　real-world　taxi　datasets　demonstrates　the　generalization　of　LRS4DP　under　different　urban　scales　and　layouts.　The　LRS4DP　framework　is　also　generically　applicable　for　location　prediction　tasks　(e.g.,　next　location　and　passing-by　location　predictions)　and　capable　to　support　various　downstream　transportation　and　location-based　service　applications.　©　2023　IEEE.