Message Personalization via Knowledge Graph and ML Impact Prediction
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Solution Overview
Problem
Existing communication systems fail to effectively personalize messages for individual recipients, leading to misunderstandings and miscommunications due to lack of consideration for the complete context of the recipient, despite efforts like sentiment analysis and context-based virtual assistants.
Innovation Solution
A method and system that semantically analyze communication history between sender and receiver to form a knowledge graph, derive formality level values, and use machine-learning models to predict receiver impact scores, modifying linguistic expressions to optimize message impact, involving multiple machine-learning models and techniques like reinforcement learning and BERT for dynamic message personalization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If highly complex sentiment analyses are performed prior to marketing campaigns to send context-specific messages to individual receivers, then message personalization and impact are improved, but system complexity and effort are significantly increased
Solution Approach 1:
The system performs preliminary semantic analysis of communication history between sender and receiver to form a knowledge graph and derive formality level values before the actual message sending. This pre-computed contextual understanding is stored and reused for multiple messages, avoiding repeated complex analyses while maintaining high personalization quality
Solution Approach 2:
The system creates a simplified representation (knowledge graph) that copies and stores the essential contextual patterns from communication history. This copied structural knowledge can be efficiently queried and applied to personalize messages without re-processing the entire communication history each time
2Productivity
If messages are sent without considering the complete context of the recipient, then communication speed and simplicity are maintained, but misunderstandings and miscommunications increase
Solution Approach 1:
The system introduces a knowledge graph as an intermediary layer between the raw communication history and the message personalization process. This intermediary structure captures the essential contextual relationships and formality levels, enabling fast and accurate message tailoring without directly processing the entire communication history for each message
3Quantity of substance
If sender uses non-concentrated sending approach to reach large audience, then coverage and reach are improved, but message impact varies significantly across receivers
Solution Approach 1:
The system applies local quality by tailoring each message to the specific receiver's contextual relationship with the sender. The knowledge graph stores receiver-specific formality level values and communication patterns, enabling the system to automatically adjust message tone, style, and content for each individual receiver while maintaining consistent personalization quality across the entire audience
Data Source
AI summary
A method for personalizing a message between a sender and a receiver is provided. The method comprises semantically analyzing a communication history to form a knowledge graph, deriving formality level values using a first trained ML model, analyzing parameter values of replies to determine receiver impact score, and training a second ML system to generate a model to predict the receiver impact score value. The method also comprises selecting a linguistic expression in a message being drafted, determining an expression intent, modifying the linguistic expression based on the formality level and the expression intent to generate a modified linguistic expression, and testing whether the modified linguistic expression has an increased likelihood of a higher receiver impact score. The method also comprises repeating selecting the linguistic expression, determining the expression intent, modifying the linguistic expression, and testing until a stop criterion is met.