Imagine an AI that not only suggests movies you might like but also recommends video games based on your film preferences. This is the challenge of cross-domain sequential recommendation (CDSR). A new research paper explores how to make this a reality, especially for users with limited history in one of the domains (the "cold-start" problem). Traditionally, AI models struggle to bridge the gap between different domains, like movies and video games, because they rely heavily on user data within each domain. This new research proposes a framework called URLLM that leverages the power of large language models (LLMs) to understand the semantic connections between items across domains. URLLM introduces a clever two-step process. First, it builds a detailed "map" of items and their attributes, connecting movies and games based on shared characteristics like genre or theme. It also models user behavior within each domain and learns how preferences in one area might translate to another. Second, URLLM retrieves information about similar users to provide the LLM with relevant examples, effectively teaching it how to recommend across domains. To avoid recommending items from the wrong domain, like suggesting a movie when a game is expected, URLLM uses a refining process that filters and adjusts the LLM's output. Tested on Amazon datasets spanning movies, games, art supplies, and office products, URLLM consistently outperformed existing recommendation models. This suggests URLLM is particularly good at helping new users in a domain by drawing on their behavior elsewhere. The research shows that understanding semantic relationships and using similar-user information is key to successful cross-domain recommendations, a crucial step towards more intelligent and personalized AI systems.