Dynamic Indicator Weighting for Search Item Scoring
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Solution Overview
Problem
Existing search engines use fixed weights for indicator values when scoring resources, which can lead to reduced variance in item scores and difficulty in emphasizing different indicators for different items, potentially resulting in similar scores for distinct resources.
Innovation Solution
A search engine generates scores by combining indicator values with weights determined by indicator-specific functions, allowing for dynamic weighting based on the value of each indicator, which can emphasize different indicators for different items and reduce the likelihood of identical scores for distinct resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If fixed weights are used for indicator values, then the scoring process is simple and stable, but the variance of item scores is reduced and different items cannot be differentiated effectively
Solution Approach 1:
The patent applies dynamics by transitioning from fixed weights to dynamic weights that are calculated based on indicator-specific functions. Each indicator value is transformed into a weight through a function f_i(v_i), allowing the weighting system to adapt to different items while maintaining a stable scoring framework. This resolves the contradiction by making weights flexible rather than static.
Solution Approach 2:
The patent changes the parameter of weights from constant values to variable values derived from indicator functions. By defining weights as w_i = f_i(v_i) where v_i is the indicator value, the system can adjust weights based on actual item characteristics, enabling better differentiation while preserving scoring stability through the functional relationship.
2Adaptability or versatility
If dynamic weighting based on indicator values is used, then item differentiation and score variance are improved, but the complexity of the scoring system increases
Solution Approach 1:
The patent transforms the complex problem of individualized weighting into a manageable solution by parameterizing weights through functions of indicator values. Instead of manually setting weights for each item, the system uses mathematical functions f_i(v_i) that automatically generate appropriate weights, reducing operational complexity while maintaining adaptability.
Solution Approach 2:
The scoring system performs self-service by automatically determining weights through indicator-specific functions without requiring manual intervention for each item. The functions f_i(v_i) enable the system to self-adjust weights based on the characteristics of each item, reducing the burden of system management while maintaining high differentiation capability.
3Productivity
If fixed weights are applied to all items, then the scoring process is computationally efficient, but the accuracy of representing item quality varies is reduced
Solution Approach 1:
The patent improves quality representation accuracy by changing weights from fixed parameters to dynamic parameters based on indicator values. The functional relationship w_i = f_i(v_i) allows the system to capture variations in item quality more precisely while maintaining computational efficiency through straightforward function evaluations rather than complex calculations.
Solution Approach 2:
The patent enhances measurement precision by introducing dynamics into the weighting process. Instead of using static weights that cannot adapt to different items, the system employs dynamic weights derived from indicator-specific functions, enabling accurate representation of varying item qualities while keeping the computational process efficient through direct function application.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for scoring items. One method includes generating a score for each of a plurality of items. The generating includes, for each item: obtaining a respective item-specific value of each of a plurality of indicators, determining a respective weight for the value of each of the plurality of indicators, wherein the respective weight for one or more of the plurality of indicators is derived from the value of the indicator, and generating the score for the item from the item-specific values and weights for the item-specific values. The method further includes providing the score for each item to a ranking engine implemented on one or more computers.