Cold-start recommendation, which addresses the challenge of recommending items to new users without sufficient historical interaction data, remains difficult in personalized recommender systems. Cross-domain recommendation methods have gained attention in addressing this issue by transferring user preferences from a source domain to alleviate the cold-start problem in a target domain. Traditional embedding-based approaches typically rely on a one-to-one alignment over continuous user embeddings using overlapping user IDs, which often leads to severe overfitting and limited generalization, particularly when overlapping users between domains are sparse. In this paper, we propose a novel hierarchical recommendation framework specifically targeting the cold-start problem by modeling user preferences as hierarchical interests derived from textual embeddings and Residual Quantized Variational Autoencoders (RQ-VAE). Unlike traditional methods that directly align embeddings, our approach builds a mapping function transferring hierarchical user interest structures from the source to the target domain. This hierarchical mapping significantly enhances generalization, providing a more comprehensive and robust representation of user preferences. Additionally, we employ a generative rewriting mechanism utilizing Large Language Models to refine user-generated reviews into concise, semantically enriched summaries that explicitly highlight user interests. Extensive evaluations on Amazon review datasets demonstrate the effectiveness of our hierarchical preference modeling and generative rewriting approach, outperforming existing embedding-based methods consistently, especially in cold-start and sparsely populated scenarios. Our proposed method thus provides a robust, flexible solution for personalized recommendation tasks in cold-start conditions.