Metaverse-Oriented User Preference Recommendation Systems Based on DSD-Transformer

The Metaverse, with its promise of immersive experiences and transformative user interactions, represents a new paradigm for IoT development. By integrating IoT with the Metaverse, real-world data can seamlessly enrich virtual environments, offering diverse choices to users. However, the sheer volume of products and user groups in the Metaverse poses challenges in effectively matching users with suitable products. Recommendation systems, particularly Collaborative Filtering (CF), emerge as a solution to this issue, leveraging user preferences and social dynamics. However, traditional CF algorithms face efficiency challenges in the multi-dimensional data landscape of the Metaverse. To address this, a clustering-based CF algorithm is proposed, enhancing recommendation efficiency by leveraging social connections. Additionally, the recommendation system is enhanced with a DSD-Transformer framework, optimizing recommendation accuracy. The experiments indicate that our proposed method may considerably enhance the Metaverse experience when compare to various sophisticated methods and can be utilized to build a range of product recommendation systems.