Method and system for personalizing metaverse object recommendations or reviews
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Solution Overview
Problem
Existing systems lack effective methods for personalizing metaverse object recommendations and reviews, relying on user-provided text-based comments or shallow profile data, which can be subjective and inaccurate, leading to sensory overload and difficulty in identifying relevant objects.
Innovation Solution
An immersion evaluation platform that generates personalized recommendations and reviews based on user context, metaverse object usage information, and social cohort interactions, using machine learning to analyze user profiles, location, and engagement metrics, and provides selective filtering to conserve resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If personalized recommendations are generated using comprehensive user context and machine learning analysis, then recommendation accuracy and user experience are improved, but system complexity and computational resources increase
Solution Approach 1:
The system segments the recommendation generation process into distinct modules: user context analysis module, metaverse object usage information processing module, social cohort interaction analysis module, and machine learning model module. Each module handles specific aspects of the recommendation task, making the overall complex system manageable and maintainable while achieving high recommendation accuracy through coordinated operation of these specialized components.
2Measurement precision
If comprehensive user context and usage information are analyzed, then recommendation relevance is improved, but data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing user context information, metaverse object usage information, and social cohort interaction data in structured formats before recommendation generation. Machine learning models are trained in advance on historical data, so when recommendations are needed, the system can quickly query and combine pre-processed information rather than analyzing raw data from scratch, significantly reducing real-time processing time while maintaining high recommendation relevance.
3Productivity
If selective filtering is implemented to conserve resources, then system efficiency is improved, but information completeness may be reduced
Solution Approach 1:
The system dynamically adjusts filtering parameters based on user context, object importance, and resource availability. Rather than applying fixed filtering rules that might remove valuable information, the system uses machine learning to determine which parameters and attributes are most relevant for each specific recommendation scenario, adjusting the filtering threshold and criteria to maintain information completeness while still achieving resource conservation through intelligent selective processing.
Data Source
AI summary
Aspects of the subject disclosure may include, for example, obtaining contextual information associated with a user, wherein the user is engaged in an immersive environment using a target user device, and wherein the contextual information comprises user profile data, data regarding a location of the user, data regarding one or more inputs provided by the user, or a combination thereof, receiving data regarding a metaverse object in the immersive environment, determining a relevance of the metaverse object to the user based on the contextual information and the data regarding the metaverse object, responsive to the determining the relevance of the metaverse object to the user, generating a personalized recommendation or review of the metaverse object for the user, and causing the personalized recommendation or review to be provided to the user in the immersive environment for user consumption. Other embodiments are disclosed.