User Language Model Retraining for Social Presence Simulation
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Solution Overview
Problem
Users on social networking platforms experience a significant impact when other users are absent, especially if the user is deceased, as they no longer receive content from that user, affecting the user experience and continuity.
Innovation Solution
A language model is trained using user interaction data to simulate the user's presence, allowing the social networking system to generate responses and interactions on their behalf, even when the user is absent or deceased.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a user is absent from the social networking platform for a long period or deceased, then the user experience of connected users deteriorates as they no longer receive content from the absent user, but implementing traditional solutions (waiting for user return) is not feasible for permanent absence
Solution Approach 1:
The patent creates a digital twin or simulation of the absent user using machine learning models trained on the user's historical interaction data. This copy reproduces the user's typical behaviors, responses, and content creation patterns, allowing connected users to interact with content that appears to come from the absent user, thereby maintaining experience continuity without requiring the original user's physical presence
Solution Approach 2:
The system transforms static user profile data into dynamic simulation parameters by training ML models on historical interaction patterns. The simulation adapts its behavior parameters (response styles, content topics, interaction frequency) based on the trained model, enabling the absent user's digital representation to maintain consistent behavioral characteristics over time
2Reliability
If the social networking system implements user simulation using machine learning models, then user experience continuity is maintained during absence, but system complexity increases due to model training and deployment requirements
Solution Approach 1:
The system performs user simulation model training in advance, during periods when the user is actively using the platform. Historical interaction data is collected and used to train ML models before the user's absence begins. This preliminary preparation ensures that when the user becomes absent, the simulation model is already ready to deploy, eliminating the need for complex real-time training during the absence period
Solution Approach 2:
The patent introduces an intermediary simulation layer between the absent user and the social networking system. This intermediary component handles content generation and interaction simulation, mediating between the system's content delivery mechanisms and the absent user's historical behavior patterns. The intermediary abstracts the complexity of ML model integration from core system components
3Measurement precision
If the social networking system collects user interaction data for training language models, then simulation accuracy improves, but user privacy concerns increase due to extensive data collection requirements
Solution Approach 1:
The system applies different data collection and processing qualities to different types of user interactions. Sensitive personal information is handled with higher privacy protection (anonymization, aggregation) while public interaction patterns (likes, shares, public comments) are used with greater detail for training. This local quality differentiation maintains simulation accuracy for public-facing content while protecting private user data
Data Source
AI summary
A social networking system simulates a user using a language model trained using training data generated from user interactions performed by that user. The language model may be used for simulating the user when the user is absent from the social networking system, for example, when the user takes a long break or if the user is deceased. The social networking system receives a language model that is pretrained and retrains the language model using user specific training data based on user interactions performed by a particular user with the social networking system. The social networking system deploys the language model retrained using the user specific training data so that a bot can invoke the language model for generating responses on behalf of the user in response to content items posted by other users.