Personalized recommendation systems play a vital role in online travel platforms by helping users navigate the vast number of available accommodations. Traditional collaborative filtering methods, especially matrix factorization, are widely used but limited in capturing complex user–hotel interactions due to their linear nature. This study proposes a DNN–based embedding framework for hotel recommendation that learns user and hotel representations directly from sparse rating data and predicts ratings through a regression layer. We evaluate the model on a real-world TripAdvisor dataset containing and compare it with classical (CF_user, CF_item, SVD, ALS) and modern neural baselines (NCF, RNN4Rec). Experimental results demonstrate that even this simplified DNN architecture achieves competitive, and in many cases superior, performance, delivering lower MAE and RMSE and higher \(R^2\) scores than all baselines while maintaining manageable computational requirements. These findings highlight the potential of lightweight deep learning models for practical hotel recommendation tasks and pave the way for future extensions incorporating multi-criteria ratings and hybrid features.