RECOMMENDATION SYSTEMS INTEGRATING EXTENDED REALITY TO GENERATE PERSONALIZED RECOMMENDATIONS

Smart overview of the Invention

The system described integrates extended reality (XR) with recommendation systems to provide personalized recommendations. It comprises a memory and a processing device that work together to receive user requests for personalized recommendations. The system collects user data from various data sources and utilizes machine learning models to generate personalized recommendations. These recommendations, along with XR representations, are made accessible to users through XR systems.

Technical Field and Background

This technology pertains to database management and specifically to recommendation systems that incorporate XR for generating tailored recommendations. Enterprises often operate in environments with interconnected devices, using private networks for communication. These environments may include on-premises or remote networks, facilitating data access and analytics. Technology services in these settings can be provided internally or through third-party vendors, covering services like SaaS, IaaS, and PaaS.

Recommendation System Features

The recommendation system employs machine learning models to analyze user data and generate personalized recommendations. Traditional rule-based models often rely on historical data, which limits their ability to adapt to new trends or user-specific needs. The proposed system addresses these limitations by considering a broader range of factors, including external influences and social interactions, offering a more comprehensive view of potential future scenarios.

Data Sources and XR Integration

Data for generating recommendations is sourced from both non-XR and XR systems. Non-XR sources include websites, applications, and IoT devices, while XR sources involve virtual reality (VR), augmented reality (AR), and mixed reality (MR) systems. XR environments provide immersive experiences where users can interact with virtual spaces and objects, enabling the collection of detailed user behavior and preference data for more accurate recommendations.

Digital Representation and Feedback

The system creates digital representations of personalized recommendations, which can be shared with others. These representations may be in XR formats or other media types like audio or visual formats. Users can interact with these representations through XR systems, providing feedback that helps refine future recommendations. The feedback loop ensures that the system adapts to changing user preferences and behaviors over time.