Language Model User Simulation for Social Feed Continuity
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Solution Overview
Problem
Users on social networking platforms experience a significant impact when a connected user is absent for a long time, especially if deceased, as they no longer receive content from that user, affecting their experience and engagement.
Innovation Solution
A language model is trained using user interaction data to simulate the user's presence by generating responses and interactions on their behalf, utilizing a bot to monitor and engage with the platform based on the user's past behavior and preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a user is absent from the social networking platform for a long time or deceased, then the user experience of connected users deteriorates as they no longer receive content from the absent user, but simulating the absent user using a language model increases system complexity and raises ethical concerns
Solution Approach 1:
The patent creates a simulated version of the absent user by training a language model on the user's historical interaction data. This copy reproduces the user's communication patterns, preferences, and behavior to generate responses and interactions that mimic the original user, thereby maintaining continuity for connected users without requiring the actual user to be present
Solution Approach 2:
The language model is trained in advance on the absent user's historical data before simulation begins. This preliminary training phase captures the user's interaction patterns, content preferences, and communication style, enabling the model to generate authentic-seeming responses without real-time intervention or complex runtime decision-making
2Measurement precision
If training data is collected from user interactions to train the language model, then the simulation accuracy improves, but user privacy and data security concerns increase
Solution Approach 1:
The patent implements differential privacy techniques that add controlled noise to specific parts of the training data pipeline. Different levels of privacy protection are applied to different types of data, and the noise is calibrated to preserve simulation accuracy for public interactions while protecting sensitive private communications from being used in training
3Productivity
If the language model generates responses on behalf of the absent user, then user engagement is maintained, but the authenticity and transparency of interactions are compromised
Solution Approach 1:
The system implements feedback mechanisms where connected users can report suspicious or inauthentic interactions. This feedback is used to continuously refine the language model's behavior, improve detection of simulation artifacts, and adjust the simulation strategy to better balance engagement with authenticity. The feedback loop also helps identify edge cases where the simulation may be misleading
Data Source
AI summary
A method for generating an automated interaction on a social networking system includes receiving, from a newsfeed presented to a target user of the social networking system, a set of content items relevant to the target user. The method also includes identifying a second content item from the set of content items based on a ranking of the second content item relative to rankings of other content items. The method further includes generating a prompt that requests a predicted user interaction with the second content item, the predicted user interaction being an interaction that the target user would be expected to perform upon viewing the second content item. The method also includes receiving the predicted user interaction based on transmitting the prompt to a language model. The method further includes interacting with the second content in accordance with the user interaction.