Transfer Learning for Multistage Ranking Models
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Solution Overview
Problem
Existing online services face challenges in providing an optimal user experience across different devices and platforms, as content presentation is often based on chronological order rather than user preferences, leading to frustration and reduced user interaction.
Innovation Solution
The implementation of a multistage ranking system using transfer learning, which leverages user interactions on one platform to inform content presentation on another, employing machine-learning models like gradient boosted decision trees and neural networks to personalize feeds by identifying preferred content and behaviors across platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If content is presented in chronological order across different devices, then the system complexity is reduced, but user engagement and personalization deteriorate
Solution Approach 1:
The patent segments the ranking system into multiple stages: a first ranking model processes content for a first device, a second ranking model processes content for a second device, and these models are trained separately using transfer learning. This segmentation allows each model to be optimized for its specific device context while reducing overall system complexity through modular architecture.
Solution Approach 2:
The patent changes parameters by using transfer learning to adapt model weights and hyperparameters from one device context to another. The system adjusts ranking parameters based on device-specific user behavior patterns, enabling personalization without requiring complete retraining of ranking models for each device.
2Measurement precision
If device-specific models are trained separately, then prediction accuracy improves, but data processing requirements increase
Solution Approach 1:
The patent applies preliminary action by pre-training a general ranking model on aggregate user interaction data from multiple devices before fine-tuning device-specific models. This preliminary training establishes a strong baseline that reduces the amount of additional data processing needed for device-specific adaptation, improving efficiency while maintaining accuracy.
Solution Approach 2:
The patent uses transfer learning as an intermediary mechanism that bridges general user behavior patterns and device-specific preferences. The pre-trained model serves as an intermediary that captures universal ranking signals, which are then adapted to device-specific contexts, reducing the computational burden of processing raw data for each device separately.
3Ease of operation
If chronological ordering is used without personalization, then ease of operation is maintained, but user engagement deteriorates
Solution Approach 1:
The patent implements dynamics by making the content ranking adaptive rather than static. The system dynamically adjusts content ordering based on real-time user interactions and device-specific patterns captured by the ranking models, allowing the feed to evolve with user preferences while maintaining operational simplicity through automated model-based ranking.
Data Source
AI summary
Systems and techniques for a transfer model learning for relevance models are described herein. In an example, a system for member relevance prediction is adapted to collect a first data set of member interactions with the online service that occur on a first platform and train a first model using the first data set. The system for member relevance prediction may collect a second data set of member interactions with the online service that occur on a second platform. The system for member relevance prediction may predict a third data set related to member interactions using the first model and aggregate the first data set, the second data set, and the third data set. The system for member relevance prediction may train a second model for the second platform using the aggregated platform data and predict for the second platform, using the second model, online service items for the member.