## 3 The Proposed Conversational System: CORES
### General Architecture of CORES  
The architecture of the proposed system is illustrated in Figure 1. The architecture consists of the following modules:  
1. **VR shopping app:** It is designed to handle conversations between users and the system, known as the front end. It is responsible for capturing the user's inputs and visualizing the responses in the form of 3D items through the VR headset based on the Unreal Engine platform. 2. **CORES**: It is known as the back end of the system, responsible for receiving and processing users' information. It includes the following modules: - **Conversation state management (CSM):** It is responsible for identifying users' interests and extracting users' preferences, context, and emotions from users' inputs and incorporating them with the response generator module to generate responses. CSM is based on LLM, Langchain ([[https://www.langchain.com/](https://www.langchain.com/)]([https://www.langchain.com/](https://www.langchain.com/)), accessed on 11 February 2025). This Python library integrates GPT-4 models for effectively assisting users during the shopping virtual experience by applying text embedding techniques and answering questions about recommended items. CSM also uses GPT-4 models to analyze users' prompts and determine the tasks to be sent to the recommendation engine and response generator. CSM tracks the user's emotional state, preferences, and interactions, which informs the recommendation engine to offer personalized suggestions based on the user's emotions. - **Recommendation engine:** It is responsible for the computation of matching scores based on the user's attention, preferences, item genre, location, and current time. It performs the attention-based context-aware emotion-driven recommendation process to match the query with the item dataset to find the list of relevant items. These are then sent to the response generator module to generate two mapped vectors, including explanations and insights. The recommendation engine gives suggestions based on the user's emotions, shaping the conversation. If the user feels satisfied or dissatisfied, the system updates the conversation to improve future recommendations. * **Response generator:*
* This module uses the items list generated by the recommendation engine as inputs to generate recommendation responses in terms of explanations and insights. Afterwards, these responses are then transferred to the VR loader module for visualization through the VR headset. The response generator includes the following modules:
* **Recommendation explication generator:*
* It generates recommendation explanations based on the results of attention matching, social matching, and context matching. Then, it formulates the matching results with GPT-4 as recommendation explanations and sends them to the VR loader for visualization. * **Recommendation insight generator:** It uses GPT-4 to generate insights based on data collected from internal items datasets and external data sources. The recommendation insight generator responds by analyzing and transforming data into valuable information from customer feedback and purchase history to find popular products and trends for guiding the decision process. * **VR loader:*
* It sets up the VR environment by configuring VR-specific components such as cameras, controllers, and interaction mechanisms. Our VR loader module is based on the Unreal Engine platform, which offers robust support for handling and visualizing photorealistic real-word objects. The VR loader includes the following modules:
* **Scene loader:*
* it generates and sets up the VR scene, which will serve as a container for visualizing recommended items. * **3D model loader:*
* It loads the 3D model of the recommended items list into the scene. It also adjusts the position and scale of the loaded items within the scene to fit the user's visualization needs.