Response Relevance Estimation Using Context and Deep Learning
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Solution Overview
Problem
Virtual assistants face challenges in estimating response relevance due to the lack of initial usage log data, varying expertise among IT support agents, and the potential for biased or skewed data, which delays effective AI model training and implementation.
Innovation Solution
A system that transitions from an initial rule-based deployment phase to a deep learning model phase, using context and business policy initially, and then continuously updates with user interaction data, enabling adjustment between deployment and steady state phases to improve response relevance estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning models are used for response relevance estimation, then measurement precision is improved, but loss of time increases due to lack of initial usage log data
Solution Approach 1:
The system performs preliminary actions by collecting and storing usage log data, context data, and feedback data in data stores before deep learning model training is initiated. User profiles are created in advance using historical feedback data, and this pre-prepared data infrastructure enables rapid model training when needed, thus reducing the time loss associated with data collection and preparation.
Solution Approach 2:
The system implements dynamic adaptation by transitioning from rule-based deployment to deep learning model phase as data becomes available. The model training process is dynamic, continuously updating user profiles and response relevance estimation models as new usage log data accumulates, allowing the system to improve measurement precision over time while managing training efficiency through iterative learning.
2Loss of time
If rule-based deployment is used initially, then loss of time is reduced, but measurement precision deteriorates
Solution Approach 1:
The system employs a dynamic two-phase deployment strategy. In the initial phase, rule-based methods provide immediate response relevance estimation with acceptable precision for rapid deployment. As usage log data accumulates, the system dynamically transitions to deep learning models that progressively improve measurement precision. This dynamic approach allows the system to balance deployment speed with accuracy improvement over time.
Solution Approach 2:
The rule-based deployment serves as a preliminary action that enables immediate system operation before deep learning models are trained. During this initial phase, the system collects and preprocesses data in parallel, preparing the foundation for future model training while providing functional responses using rules, thus achieving rapid deployment without sacrificing eventual precision.
3Measurement precision
If user feedback data is collected and weighted, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system segments the complex data processing task into distinct modular components: usage log data collection, context data processing, feedback data weighting, user profile creation, and model training. Each module handles a specific aspect of data processing independently, which reduces overall system complexity while maintaining measurement precision through coordinated operation of these segmented functions.
Solution Approach 2:
The system introduces intermediary data structures and processing layers, including user profiles that aggregate and weight feedback data, and context data that mediates between raw inputs and model training. These intermediaries simplify the complexity of directly processing raw feedback data by organizing it into structured, weighted representations that are easier to process while improving measurement precision.
Data Source
AI summary
A method for estimating response relevance with respect to a received query includes receiving a set of user feedback items, a set of historical feedback data, and a set of context data, creating a user profile model according to the set of historical feedback data, wherein the user profile model indicates a weighting attribute based on the set of historical feedback data, weighting the set of user feedback items according to the created user profile model, creating a response relevance estimation model based on the weighted set of user feedback items, the received set of context data, and the received set of historical feedback data, and ranking one or more responses according to the created response relevance estimation model. The method may further include adjusting the user profile model and the response relevance estimation model responsive to receiving additional data.