Preference Adaptation via Implicit Feedback Loops
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Solution Overview
Problem
Consumers face challenges in selecting resources based on multiple traits without fully knowing their preferences, as existing methods require lengthy questionnaires and do not adapt to changing preferences or unexpected interests.
Innovation Solution
A system and method that receives limited initial preference information, matches resources with consumers, and updates preferences based on user interactions and activities, using collaborative filtering to provide targeted resource recommendations without requiring extensive user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a consumer requests a list of all available resources with initial selection criteria, then the consumer can review and select resources, but the method does not work well when there are many resources or if consumers do not wish to be actively involved
Solution Approach 1:
The system implements feedback loops where consumer interactions with presented resources (views, selections, rejections) are continuously monitored and used to refine preference models. This allows the system to learn from consumer behavior and improve resource matching over time, reducing the need for active consumer involvement while maintaining accuracy
Solution Approach 2:
The system performs self-adjustment by automatically updating preference weights and matching criteria based on observed consumer interactions. The matching algorithm autonomously refines resource recommendations without requiring explicit consumer input, enabling the system to serve itself in improving its matching capability
2Measurement precision
If a matching algorithm uses consumer answers to a battery of questions to provide targeted matches, then resources can be matched to preferences, but consumers may not be aware of important traits or want to answer lengthy questions
Solution Approach 1:
The system collects resource interaction data and preference information in advance, building comprehensive preference models before actual resource selection occurs. By pre-processing consumer behavior data and pre-calculating preference weights, the system reduces the immediate time burden on consumers while maintaining matching precision
Solution Approach 2:
The system replaces explicit consumer questioning with implicit behavior analysis. Instead of mechanically asking consumers to answer questions about their preferences, the system uses computational algorithms to infer preferences from observed interactions, substituting automated analysis for manual consumer input
3Adaptability or versatility
If consumers assign weights to multiple traits for resource selection, then they can trade off traits based on importance, but consumers are not always forthcoming in the weight assigned and may change their minds
Solution Approach 1:
The system continuously monitors consumer interactions with resources and uses this feedback to dynamically adjust preference weights. When consumers exhibit behavior inconsistent with stated preferences, the system learns from this discrepancy and updates the preference model, capturing true preferences that consumers may not explicitly articulate
Solution Approach 2:
The preference model is implemented as a dynamic system that adapts over time rather than a static set of weights. Preference weights are continuously updated based on new interaction data, allowing the system to capture changing consumer preferences and maintain accuracy as preferences evolve
Data Source
AI summary
A system and method updates a consumer's preference information not only as a result of explicit preference information received from the consumer, but also resulting from actions of the user.