Hybrid Linear Nonlinear Search Ranking Model
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Solution Overview
Problem
Current search engines face challenges in accurately ranking search results, as traditional ranking methods are limited by linear models that fail to capture complex user preferences and processing speed is compromised by the use of nonlinear models.
Innovation Solution
Implementing a dual-ranking system that first uses a linear model to rank information items based on feature values and then applies a nonlinear model to refine the top results, with preprocessing to enhance processing speed and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If a linear model is used for ranking search results, then processing speed is improved, but ranking accuracy and relevance are worsened
Solution Approach 1:
The ranking process is segmented into two stages: a linear model is first applied to quickly rank all retrieved documents, then a nonlinear model is applied only to the top-k results from the linear ranking. This segmentation allows the system to leverage the speed advantage of linear models for initial filtering while reserving the accuracy advantage of nonlinear models for final refinement of the most relevant results.
2Measurement precision
If a nonlinear model is used for ranking search results, then ranking accuracy and relevance are improved, but processing speed is worsened
Solution Approach 1:
Instead of applying the computationally intensive nonlinear model to all retrieved documents (excessive action), the system applies it only to the top-k results from linear ranking (partial action). This partial application of the nonlinear model provides sufficient accuracy improvement for the most relevant results while avoiding the full computational cost across all documents.
3Device complexity
If only a single ranking model is used, then device complexity is reduced, but the ability to capture complex user preferences is worsened
Solution Approach 1:
The system merges two different ranking models (linear and nonlinear) into a hybrid ranking framework. The linear model captures basic ranking patterns efficiently, while the nonlinear model captures complex user preferences and interactions. By combining both models in sequence, the system achieves both computational efficiency and sophisticated preference capture without requiring a single overly complex model.
Data Source
AI summary
Generating ranked search results includes receiving a plurality of matching information items that match a search request, ranking at least some of the plurality of matching information items using a linear ranking model that linearly combines a first plurality of feature values to obtain a first set of ranked results, ranking at least some of the first set of ranked results using a nonlinear ranking model that nonlinearly combines a second plurality of feature values to obtain a second set of ranked results, and provide a search response based on the second set of ranked results.