Relevance Group Suggestions via De-noising Auto-Encoder
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Solution Overview
Problem
Existing communication software lacks the ability to intelligently recommend relevant individuals for messages or conversations based on the context and content of prior user communications, relying on basic signals such as name initials or frequent contacts.
Innovation Solution
A system that structures records of prior user communications into 'conversation boxes' to train a prediction algorithm, such as a de-noising auto-encoder model, to derive optimal weights for recommending participants based on contextual features, and uses a scoring function to identify top-ranked participants, with feedback adjustment for improving recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If basic signals such as name initials or frequent contacts are used for recommendations, then the system is simple to implement, but the relevance accuracy is poor
Solution Approach 1:
The system performs preliminary action by pre-processing and structuring historical communication records into conversation boxes before making recommendations. This involves extracting contextual features, identifying communication patterns, and preparing training data in advance, which enables the machine learning model to achieve high relevance accuracy without increasing operational complexity during actual recommendation generation
Solution Approach 2:
The system implements feedback mechanisms where user interactions with recommended contacts are tracked and fed back into the learning model. This continuous feedback loop allows the de-noising auto-encoder to iteratively improve its weight assignments and recommendation accuracy, resolving the contradiction between simplicity and precision through adaptive learning
2Measurement precision
If machine learning models are used to analyze communication history, then recommendation relevance improves, but processing time and computational resources increase
Solution Approach 1:
The system applies preliminary action by pre-structuring communication history into conversation boxes and pre-extracting contextual features before actual recommendation queries. This preparation phase consolidates computational work into batches, reducing real-time processing requirements and enabling fast recommendations while maintaining high relevance through the trained de-noising auto-encoder model
3Measurement precision
If the system analyzes content and context of communications, then recommendation accuracy improves, but data processing complexity increases
Solution Approach 1:
The system segments the complex data processing task into distinct manageable components: conversation box structuring, contextual feature extraction, pattern identification, and recommendation generation. This segmentation allows each component to be optimized independently and simplifies the overall data processing pipeline while maintaining high recommendation accuracy through the modular architecture
Data Source
AI summary
Techniques for providing a people recommendation system for predicting and recommending relevant people (or other entities) to include in a conversation. In an exemplary embodiment, a plurality of conversation boxes associated with communications between a user and target recipients, or between other users and recipients, are collected and stored as user history. During a training phase, the user history is used to train encoder and decoder blocks in a de-noising auto-encoder model. During a prediction phase, the trained encoder and decoder are used to predict one or more recipients for a current conversation box composed by the user, based on contextual and other signals extracted from the current conversation box. The predicted recipients are ranked using a scoring function, and the top-ranked individuals or entities may be recommended to the user.