GLMix Model with Tree Interaction Features for Search Ranking
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Solution Overview
Problem
Social networking services face challenges in providing the most relevant candidate search results in a timely manner due to the vast and growing volume of information, and existing models lack personalization and non-linear feature interactions, which affects the accuracy of candidate recommendations.
Innovation Solution
The implementation of Generalized Linear Mixed Models (GLMix) with nonlinear tree interaction features, which incorporate gradient boosted decision trees to generate tree interaction features and enhance personalization by using global, per-recruiter, and per-contract models, allowing for more accurate predictions by capturing user preferences and item attractions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional search models are used to handle candidate search results, then the system can process large volumes of information, but the precision of candidate recommendations deteriorates due to lack of personalization and non-linear feature interactions
Solution Approach 1:
The patent segments the search model into multiple independent components: gradient boosted decision trees for non-linear feature interactions, generalized linear mixed models for personalization, and feature engineering modules. This segmentation allows each component to specialize in specific tasks while maintaining overall system manageability and improving recommendation precision through targeted optimizations.
Solution Approach 2:
The patent creates a composite modeling approach by combining gradient boosted decision trees with generalized linear mixed models. This composite structure integrates the strength of tree-based non-linear feature detection with the personalization capabilities of mixed models, achieving superior recommendation precision that neither component could achieve alone while managing complexity through modular integration.
2Measurement precision
If personalized models with non-linear feature interactions are implemented, then the accuracy of candidate recommendations improves, but the computational complexity and processing time increase
Solution Approach 1:
The patent applies preliminary action by pre-training gradient boosted decision trees on historical data and pre-computing feature interactions before actual candidate search. This offline preparation stores learned patterns and non-linear relationships that can be quickly applied during online candidate ranking, significantly reducing real-time processing time while maintaining high accuracy.
Solution Approach 2:
The patent implements dynamics by using gradient boosted decision trees that can adaptively learn and update non-linear feature interactions based on new data. The model dynamically adjusts feature importance and interaction patterns, allowing it to capture evolving user preferences without requiring complete retraining, thus balancing accuracy improvement with computational efficiency.
Data Source
AI summary
In an example embodiment, gradient boosted decision trees are used to generate tree interaction features, which encode a set of decision rules for features of search results and hence allow feature interactions. These tree interaction features may then be used as features of a GLMix model, essentially injecting non-linearity into the GLMix model.