Graph-Based Avatar Spawn Positioning for Virtual Experience Engagement
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Solution Overview
Problem
Conventional initial player positioning in virtual experiences often results in lack of immediate engagement, lack of adjacent activity of interest, and absence of proximate players the user is acquainted with, leading to unsatisfactory user experience.
Innovation Solution
A method involving graph-based avatar embeddings to determine optimal spawn positions by analyzing social and activity engagement data, using a social graph to map avatars and virtual activities, and calculating weighted predictions for improved player positioning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If predetermined or random initial positions are used, then positioning simplicity is maintained, but user engagement and activity relevance deteriorate
Solution Approach 1:
The system performs preliminary actions by pre-calculating optimal spawn positions based on social graph data and activity locations before the player enters the virtual experience. The server determines the best initial position in advance by analyzing friendships, recent activities, and event locations, then provides this pre-computed position to the player's device, eliminating the need for complex real-time calculations during gameplay.
Solution Approach 2:
The server acts as an intermediary between the player's social graph data and the virtual experience environment. It receives friendship data and activity information from the social graph service, processes this information to determine optimal spawn positions, and then communicates these positions back to the player's device. This intermediary processing enables personalized positioning without requiring complex client-side computations.
2Productivity
If graph-based avatar embeddings and social graph analysis are used, then player engagement and activity relevance are improved, but system complexity increases
Solution Approach 1:
The server serves as an intermediary that handles the complex graph-based processing centrally. It receives simplified friendship data from the social graph service, performs the heavy computational work of analyzing social connections and activity patterns to determine optimal spawn positions, and then returns simple position coordinates to the player's device. This centralizes complexity in the server while keeping client-side requirements simple.
Solution Approach 2:
The system creates a simplified representation of the complex social graph by extracting only the necessary friendship and activity data needed for position determination. Instead of processing the entire complex social network, it copies and works with a filtered subset of relevant data (friends within certain distance thresholds and recent activity locations), reducing computational complexity while maintaining effectiveness.
3Productivity
If social graph data and activity locations are analyzed to determine spawn positions, then player engagement is enhanced, but data processing requirements increase
Solution Approach 1:
The system applies partial action by analyzing only the portion of the social graph that is relevant to position determination - specifically friendship connections and activity locations within a certain distance threshold. It does not process the entire social network or all historical data, but rather focuses on a selective subset of data that directly impacts spawn position decisions, reducing overall data processing volume while maintaining engagement effectiveness.
Solution Approach 2:
The system performs preliminary data filtering and processing before the actual spawn position determination. It pre-identifies relevant friends and activities based on distance thresholds and temporal criteria, creating a reduced dataset that is easier to process during the actual position calculation. This preliminary action reduces the data processing burden during the critical spawn moment.
Data Source
AI summary
Implementations described herein relate to methods, systems, and computer-readable media to automatically position players within a virtual experience. A method can include generating graph-based avatar embeddings for a plurality of avatars engaged with a virtual experience hosted on a virtual experience platform, identifying avatars of the plurality of avatars that a new avatar that is not currently in a virtual environment is likely to engage with, determining possible spawn positions for the new avatar in the virtual environment, the possible spawn positions based upon respective distances between the identified avatars, calculating a plurality of spawn positions, ranking the plurality of spawn positions based on a weighted prediction of engagement, and placing the new avatar in the virtual environment at the highest ranked spawn position.