NLU Ranking Classifier Personalization via User Weights
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Natural language understanding (NLU) engines struggle to provide personalized user experiences for new users and fail to adapt to changing user interests over time, as they rely on historical data and lack user control to override personalization biases.
Innovation Solution
A method is introduced to train a ranking classifier using user-specific weights for domains of interest, allowing users to specify their preferences directly through a user interface, which influences the ranking of NLU hypotheses and adapts to changing user preferences by updating weights based on usage patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If NLU engines rely on historical data for personalization, then user experience is customized, but new users cannot be personalized and users cannot override learned biases
Solution Approach 1:
The system performs preliminary action by collecting user preference data through explicit user actions (selecting/searching for content, providing feedback) and implicit signals (usage patterns, time spent on content) before NLU processing is needed. This pre-collected data is stored and immediately applied when users provide NLU input, eliminating the need for real-time learning and enabling immediate personalization even for new users.
Solution Approach 2:
The system implements feedback mechanisms where users can explicitly provide feedback by selecting or searching for content in their areas of interest, and implicitly through usage patterns. This feedback continuously updates the user profile data, allowing the system to adapt to changing user preferences over time while maintaining the ability to override learned biases through explicit user actions.
2Adaptability or versatility
If NLU engines use historical usage data, then personalization is achieved, but users lose control to override preferences
Solution Approach 1:
The system enables users to self-serve their personalization needs by allowing them to explicitly select and search for content in their areas of interest, and to provide direct feedback. Users have full control to shape their own profiles through these actions, and can override learned preferences by simply providing new explicit feedback or searching for content in different domains.
Solution Approach 2:
The system implements dynamic personalization where user profiles are not static but continuously evolve based on new user actions and feedback. The weighting of user preferences is dynamic, allowing users to shift their interests over time by providing updated feedback, thus maintaining both personalization accuracy and user control.
3Measurement precision
If NLU engines require prolonged usage for personalization, then accurate user preferences are learned, but immediate customization is not available
Solution Approach 1:
The system performs preliminary data collection through explicit user actions (selecting/searching for content, providing feedback) and implicit signals (usage patterns, time spent on content) before NLU processing is needed. This pre-collected data is stored and immediately applied when users provide NLU input, eliminating the need for real-time learning and enabling immediate personalization even for new users.
Solution Approach 2:
The system uses partial action by collecting a limited but sufficient set of user preference signals through explicit actions and implicit usage patterns, rather than requiring prolonged usage across all possible user behaviors. This allows the system to achieve adequate personalization accuracy with minimal user interaction time, enabling immediate customization.
Data Source
AI summary
Methods and apparatus for natural language understanding (NLU) processing based on user-specified interests. Information specifying a weight for each of a plurality of domains is received via a user interface. The plurality of domains each relates to a potential area of interest for the user, and the weight for a domain from among the plurality of domains indicates a level of interest for the user in the domain. A ranking classifier used to rank NLU hypotheses generated by an NLU engine is trained using training data from which features are, at least in part, based on the information specifying a weight for each of the plurality of domains.