Multilabel Learning for Massive Categorization of Resources in Metaverse across SmartX Environments

The much-anticipated metaverse will fundamentally transform how consumers interact with machines across smartX environments. Information overload is a problem that Metaverse users can encounter when they navigate a myriad of multimedia resources. Providing effective and timely categorization of resources can facilitate effective human-human communications in Metaverse. Existing categorization techniques for current web have four key limitations: segregation of feature selection and feature correlation, validation using small datasets, lack of comparative analysis of multilabel classifiers, and lack of framework for selecting optimal classifiers. These limitations hinder existing methods' applicability to Metaverse. In response, this paper presents an effective framework for multilabel categorization of Metaverse resources using natural language processing and machine learning. The proposed framework comprises several major steps including extraction of contents, preprocessing, removing redundant and ambiguous data, converting text into features, then feature selection and classification. The proposed framework has been implemented as a predictive model for Metaverse users and tested on benchmark and real-world datasets. Experimental evaluation is conducted based on multiple parameters: accuracy, average precision, Hamming loss (HLoss), one error (1Error), ranking loss (RLoss). Experimental results show maximum increase in accuracy of 10% for both datasets. Comparative analysis indicates the proposed framework outperforms existing models not only by accuracy but also with advanced evaluation measures such as Hamming loss, ranking loss, and one error