Transfer Learning for Position Seniority Modeling With Sparse Labels
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Solution Overview
Problem
Current methods for determining user position seniority in online services are inaccurate due to the lack of sufficient training data for neural networks, leading to errors in content and recommendation systems.
Innovation Solution
Utilize transfer learning to pre-train a neural network with standardized position titles and fine-tune it with limited labeled position seniority data, leveraging the relationship between title standardization and seniority inference to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a neural network is trained to determine position seniority using limited labeled data, then the system can infer seniority from user profiles, but the accuracy is insufficient due to data shortage
Solution Approach 1:
The patent applies preliminary action by pre-training the neural network on a large corpus of standardized position titles before fine-tuning with limited labeled seniority data. This pre-training phase establishes foundational knowledge about position hierarchies and relationships, enabling the model to achieve better accuracy despite the scarcity of labeled seniority annotations.
Solution Approach 2:
The patent introduces standardized position titles as an intermediary between raw user profile data and position seniority determination. The neural network first learns to standardize position titles from various formats, then uses this standardized representation to infer seniority levels. This intermediary step enriches the training process and improves final accuracy.
2Ease of operation
If standardized position titles are used to determine seniority, then the process is simplified, but accuracy decreases because raw position titles do not accurately represent actual seniority levels
Solution Approach 1:
The patent applies parameter changes by transforming the neural network's processing approach: instead of directly mapping raw position titles to seniority levels, the model learns to extract and weight multiple features from both standardized titles and raw title characteristics. This parameter transformation enables the system to maintain simplicity while improving accuracy by considering both standardized and raw title information.
Data Source
AI summary
Techniques for using transfer learning to address label data shortage in seniority modeling for an online service are disclosed herein. In some embodiments, a computer-implemented method comprises training an initialized neural network using training examples comprising profile data and labels for the profile data, where each label comprises a standardized position title, and the training of the initialized neural network forms a pre-trained neural network. Next, the computer system may train the pre-trained neural network using training examples comprising profile data and labels for the profile data, where the labels comprise a position seniority, and the training of the pre-trained neural network forms a fine-tuned neural network. The computer system may then compute the position seniority for a user based on profile data of the user using the fine-tuned neural network, and use the position seniority of the user in an application of an online service.