## 1 Introduction  
Global e-commerce is evolving rapidly, covering a broader range of online shopping activities and intensifying market competition. To thrive in this field, companies must harness modern artificial intelligence (AI) technologies to engage customers, uncover valuable insights, and enhance customer experience. The widespread adoption of AI-powered recommender systems highlights their crucial role in modern online shopping. By providing relevant products suggestions, AI solutions enhance engagement and the shopping experience. The main recommender systems used in the context of online shopping are content-based [1], collaborative filtering [2], deep learning [3], hybrid, and context-aware recommendation [4]. With the increasing variety of available products, clients seek customized products that align with their intentions, desires, and emotions. In response, online shopping is shifting toward virtual reality (VR) technology, with the introduction of immersive virtual shopping experiences (VSEs) [5] through 3D product models. VR enables users to explore products in a simulated real-world environment, providing a deeper understanding of their features. Several studies have highlighted the impact of VR avatars on users' decision-making behavior [6], offering valuable insights into products and purchasing processes. Despite its benefits, VR-based online shopping often lacks contextual cues such as shopping context, delivery time, users' intentions, positive emotion responses towards products, and past experiences or interactions. These factors are essential for delivering effective recommendations. In the rapidly evolving retail landscape, technology has revolutionized how consumers interact with products and brands. This case study explores VRShop, a VR shopping platform that enables users to experience immersive shopping across various stores. The platform features an advanced recommendation system that enhances users' experiences by suggesting relevant products based on their preferences and behaviors. VRShop was developed to meet the growing demand for innovative online shopping experiences. VRShop bridges the gap between traditional e-commerce platforms and physical stores, combining convenience with engaging realistic interactions. With the emergence of the large language model (LLM) [7], personalized item recommendation can be effectively enhanced, as LLMs excel in generative capabilities and information integration abilities [8; 9]. However, LLMs face challenges such as insufficient domain-specific knowledge, which can result in inaccuracies or incomplete responses. These challenges become apparent when processing diverse inputs including social media data, large product catalogs, and varied user feedback. Retrieval-augmented generation (RAG) offers a solution to these challenges by retrieving external contextually enriched data and incorporating them into responses [10]. For instance, [PERSON] et al. [11] proposed a framework that uses structured knowledge bases to enhance RAG in dialogue systems. RAG has also proven effective in domains like healthcare, education, and law [12; 13; 14], supporting robust data retrieval and content generation. However, these approaches lack contextual sensitivity to personal emotions, individual preferences, and behavioral tendencies. Furthermore, they have not been effectively adapted to practical use within VR environments, limiting their ability to deliver deeply personalized and immersive recommendations. To address these shortcomings, we introduce CORES, a novel context-aware emotion-driven recommendation system for VR shopping experiences, which integrates users' profiles and contextual emotions to improve recommendations. CORES leverages the power of LLM by amalgamating VR context, users' emotional states, and dataset inputs to recommend relevant and personalized items. The system also uses 3D reconstruction techniques to visualize recommended items in a VR environment.