Adaptive Similarity Model Construction via Subset Selection
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Solution Overview
Problem
Conventional techniques for capturing similarity among items are time-consuming and costly, as they require extensive user input and domain-specific features that do not reflect human perception of similarity, making them inefficient for various applications.
Innovation Solution
A method and system for constructing a similarity model based on human-performed similarity evaluations, where items are adaptively selected for evaluation, reducing the need for extensive user input and domain expertise, using an iterative process to optimize the selection and evaluation of items for similarity ratings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional techniques are used to construct similarity models with extensive user input, then measurement precision of similarity is improved, but loss of time and cost increase significantly
Solution Approach 1:
The system performs preliminary actions by automatically pre-selecting subsets of items that are most informative for similarity model construction, based on initial analysis of item characteristics. This preliminary selection reduces the overall number of user evaluations needed, thereby decreasing time loss while maintaining measurement precision.
Solution Approach 2:
The system implements feedback mechanisms where user similarity evaluations are continuously incorporated to refine and update the similarity model. This iterative feedback process allows the model to improve measurement precision over time with progressively fewer user inputs, as the model learns from each evaluation and becomes more accurate.
2Manufacturing precision
If domain-specific features are used to represent item characteristics, then manufacturing precision of similarity representation is improved, but device complexity and ease of manufacture worsen
Solution Approach 1:
The system enables self-service by automatically selecting item subsets and constructing similarity models without requiring domain expertise from users. The algorithm autonomously identifies informative item characteristics and builds the similarity representation, thereby maintaining manufacturing precision while reducing device complexity and ease of manufacture.
3Productivity
If adaptive selection of item subsets is implemented, then productivity of similarity model construction is improved, but measurement precision of similarity evaluation may worsen
Solution Approach 1:
The system performs preliminary analysis to identify and select the most informative item subsets for evaluation. By pre-selecting items that provide maximum information gain, the system achieves high productivity in model construction while maintaining measurement precision, as the selected subsets are specifically chosen to be representative and informative.
Solution Approach 2:
The system dynamically changes parameters such as subset selection criteria and evaluation thresholds based on the construction progress and model performance. This adaptive parameter adjustment allows the system to optimize both productivity and measurement precision, balancing speed and accuracy throughout the model construction process.
Data Source
AI summary
A method, system, and computer-readable storage medium for computing a representation of similarity among items in a set of items. Computing a representation of similarity items may comprise generating a first similarity model that represents characteristics of the set of items, the characteristics being indicative of similarity among the items in the set of items. Additionally, computing the representation of similarity may comprise adaptively selecting a subset of the set of items for similarity evaluation based on the first similarity model, receiving a similarity evaluation for the adaptively-selected subset of items, and generating a second similarity model based on the first to similarity model and the received similarity evaluation.