Response Recommendation System Using Online Learning
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Solution Overview
Problem
Existing response recommendation systems in cloud platforms are limited by requiring labeled inputs and static response sets, which are time-consuming and costly, as agents often provide repetitive responses to similar inquiries, and do not account for various types of user inquiries.
Innovation Solution
A response recommendation system trained on unlabeled historical conversation data using word embedding functions to generate context and response vectors, allowing for dynamic updates and customization, enabling agents to provide relevant responses without extensive retraining.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If agents manually create and update response sets, then response relevance can be improved, but time consumption and cost increase
Solution Approach 1:
The system enables self-service by allowing agents to mark responses as custom without requiring manual retraining of the entire model. The model automatically learns from these individual custom responses through online learning, eliminating the need for agents to manually update the entire response set while maintaining high relevance.
Solution Approach 2:
The system changes the parameter of model training from batch retraining to online learning. Instead of requiring complete retraining when new responses are added, the model updates its parameters incrementally based on custom responses marked by agents, significantly reducing time consumption while maintaining response relevance.
2Stability of the object's composition
If agents reply with the same or similar responses to customer inputs, then response consistency is improved, but productivity decreases due to repetitive work
Solution Approach 1:
The system implements feedback by having agents mark responses as custom when they differ from recommendations. This feedback loop allows the model to learn from actual agent behavior and improve future recommendations, reducing repetitive work while maintaining consistency through data-driven optimization rather than manual rule-setting.
Solution Approach 2:
The system copies successful response patterns from historical data and similar conversations to generate recommendations. By analyzing and copying effective response structures from the training data, the system provides consistent recommendations that reduce the need for agents to create responses from scratch, thereby improving productivity.
3Measurement precision
If the system uses labeled conversation data for training, then model accuracy is improved, but data preparation complexity and time increase
Solution Approach 1:
The system converts the previously harmful requirement for labeled data into a benefit by using unlabeled historical conversation data for training. The model learns patterns from the structure and content of conversations themselves, treating what was once a limitation (unlabeled data) as the primary training resource, thereby eliminating complex data preparation while maintaining accuracy.
Solution Approach 2:
The system performs self-service by automatically generating training data from historical conversations without requiring manual labeling. The model itself identifies patterns and creates training representations from the raw conversation data, eliminating the need for complex data preparation processes while maintaining high training accuracy.
4Reliability
If the system requires retraining when custom responses are added, then response accuracy is improved, but time consumption increases
Solution Approach 1:
The system implements dynamics by transitioning from static batch training to dynamic online learning. When agents mark responses as custom, the model dynamically updates its parameters in real-time without requiring complete retraining. This dynamic approach maintains response accuracy while significantly reducing the time needed to incorporate new responses.
Solution Approach 2:
The system performs preliminary action by continuously learning from custom responses as they are marked during conversations. Instead of waiting for a complete retraining cycle, the model preliminarily adapts to new response patterns immediately, maintaining accuracy without requiring time-consuming periodic retraining.
Data Source
AI summary
A data processing system analyzes a corpus of conversation data received at an interactive conversation service to train a response recommendation model. The response recommendation model generates response vectors based on custom responses and using the trained model and generates a context vector based on received input at the interactive conversation service. The context vector is compared to the set of response vectors to identify a set of recommended responses, which are recommended to an agent conversing with a user using the interactive conversation service.