Dynamic Threshold for Job Recommendation Relevance
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Solution Overview
Problem
Social networking services face challenges in providing relevant job search results due to the abundance of data, leading to users being presented with irrelevant or insufficient recommendations, as existing recommender systems rely on static thresholds that fail to accurately capture user preferences and item attractions.
Innovation Solution
Implementing a dynamic threshold based on a harmonic mean of engagement and dismissal probabilities within a generalized linear mixed model (GLMix) framework, combined with separate models for global, user-specific, and job-specific behaviors, to improve the accuracy of job search result rankings and recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a static threshold is used in recommender systems to determine whether to display recommendations, then the system can operate efficiently with simple logic, but users may be presented with irrelevant items or too few recommendations because the threshold cannot adapt to individual user preferences
Solution Approach 1:
The patent transforms the static threshold into a dynamic, user-specific threshold. Instead of using a fixed threshold for all users, the system calculates personalized thresholds based on each user's interaction history, engagement patterns, and preferences. This allows the threshold to adapt automatically to individual users while maintaining operational efficiency through automated calculations.
Solution Approach 2:
The system changes the threshold parameter from a fixed value to a dynamically calculated value based on multiple factors including user engagement history, item characteristics, and interaction patterns. This parameter transformation enables the system to maintain simplicity in operation while achieving high precision in recommendation relevance through data-driven threshold adjustment.
2Measurement precision
If more data is collected and processed to improve recommendation accuracy, then the precision of predictions improves, but the complexity of the system increases significantly
Solution Approach 1:
The patent segments the recommendation system into multiple independent models: a global model for general patterns, user-specific models for individual preferences, and item-specific models for content characteristics. This segmentation allows the system to process data at different levels of granularity, improving prediction accuracy while managing complexity through modular architecture where each segment can be developed and maintained independently.
Solution Approach 2:
The system applies different levels of modeling complexity to different aspects of the recommendation problem. Rather than using a single complex model for all predictions, it uses simple global patterns where applicable, enhances them with user-specific adjustments where needed, and applies item-specific modifications where relevant. This local quality approach optimizes the balance between accuracy and complexity by applying computational resources only where they provide the most value.
3Reliability
If a threshold system is used to filter recommendations, then fewer irrelevant items are displayed to users, but users with low-scoring items may be presented with too few or no recommendations
Solution Approach 1:
The system dynamically adjusts the threshold parameter based on user-specific characteristics and context. For users who tend to have lower overall scores but may still have valuable recommendations, the threshold is adjusted downward to ensure they receive adequate recommendations. This parameter adaptation maintains high reliability by filtering out clearly irrelevant items while preserving productivity by ensuring users receive sufficient recommendations tailored to their individual patterns.
Data Source
AI summary
In an example the output of a machine learned model is a score is then compared to a threshold, and if the score transgresses the threshold, the corresponding item is available to be recommended to the user via the graphical user interface. In an example embodiment, rather than a fixed (static) threshold, a dynamic threshold is utilized. This dynamic threshold is based on a harmonic mean of probabilities utilized in the GLMix model. Specifically, the GLMix model may calculate and utilize the probability that a user will engage with a particular item via a graphical user interface, and also a probability that a user will dismiss a particular item via a graphical user interface.