Abstract:
"The selection of furniture plays a crucial role in interior design, as it not only contributes to the aesthetics of a space but also reflects the personality and preferences of the occupants. However, interior designers often face challenges in understanding the thought process and personality of their clients, making it difficult to select furniture designs that truly resonate with them.
In this project, we aim to address this problem by developing a personality-based furniture recommendation system using 3D visualization technology. The system leverages insights from personality psychology to provide convenient and tailored furniture suggestions for interior design, enhancing the overall design experience for occupants.
The project involves the use of machine learning algorithms and data mining techniques to analyze users' personality traits and preferences. By integrating 3D visualization technology, users can visualize furniture pieces in their actual space before making a purchase, allowing them to make informed decisions and ensuring a better fit with their design style and preferences.
To evaluate the effectiveness of the system, user testing and feedback will be conducted to assess the accuracy and relevance of the recommendations provided. Additionally, comparisons with existing furniture recommendation systems and user satisfaction surveys will be conducted to measure the system's performance and user acceptance.
The proposed project aims to contribute to the field of interior design by providing a novel and personalized approach to furniture selection. By leveraging the power of 3D visualization and incorporating personality-based recommendations, this system has the potential to enhance the design process, improve user satisfaction, and create living spaces that truly reflect the unique personalities and preferences of the occupants."
