Messaging System Personalized Response Filtering
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Solution Overview
Problem
Current messaging systems require users to manually type responses, which is time-consuming, and existing suggested response systems do not provide personalized messages, lacking efficiency and relevance.
Innovation Solution
A messaging system that generates personalized smart responses by analyzing historical message data and user profile information to filter and recommend responses based on a user's commonly used vocabulary and contextual factors, allowing users to quickly select suitable responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If users manually type responses in messaging applications, then response accuracy and personalization are maintained, but response generation time increases significantly
Solution Approach 1:
The system pre-generates a comprehensive list of candidate responses from historical messaging data before the user needs to reply. When a message is received, the system immediately filters and ranks pre-prepared responses based on the conversation context, eliminating the time-consuming process of generating responses from scratch and reducing response generation time while maintaining personalization through context-aware filtering
Solution Approach 2:
The system automatically analyzes the conversation context, user profile, and historical data to generate and rank personalized response suggestions without requiring user intervention. The messaging application autonomously performs context analysis, candidate selection, and personalization filtering, allowing users to simply select from suggested responses rather than manually crafting them
2Productivity
If suggested response systems use generic templates, then response generation speed is improved, but response personalization and relevance deteriorate
Solution Approach 1:
The system applies different processing qualities to different aspects of response generation: it uses efficient template-based filtering for structural elements while applying sophisticated personalization analysis to vocabulary selection and stylistic elements. Each candidate response is evaluated based on user-specific characteristics extracted from historical data, ensuring that locally important personalization attributes are preserved while maintaining overall response generation speed
Solution Approach 2:
The system pre-extracts and stores user-specific vocabulary, writing styles, and communication patterns from historical messaging data before they are needed. This preliminary analysis creates a personalized response profile that enables rapid filtering and ranking of candidate responses while maintaining user-specific characteristics, avoiding the need for time-consuming real-time personalization analysis
3Reliability
If the system filters candidate responses using personalized vocabulary, then response relevance to user style is improved, but system complexity increases
Solution Approach 1:
The system segments the response generation process into distinct modular components: candidate response generation, context analysis, personalized vocabulary filtering, and response ranking. Each module handles a specific aspect of the task independently, making the overall complex system manageable and maintainable while achieving high response relevance through coordinated operation of specialized sub-components
Data Source
AI summary
Disclosed are systems, methods, and non-transitory computer-readable media for generating personalized smart responses. A messaging system receives a message transmitted as part of a communication session between a first user and a second user and determines a set of candidate responses for replying to the message. The messaging system filters the set of candidate responses based on a personalized vocabulary for the first user, yielding a set of personalized candidate responses. The personalized vocabulary for the first user is determined based on member profile data associated with the first user. The messaging system determines, based on a set of personalized candidate selection rules, a subset of the candidate responses, yielding a set of recommended responses to the message, and causes the set of recommended responses to be presented on the second client device.