Cohort-Based GLMIX Model for Job Search Personalization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current job search algorithms face challenges in personalizing results for new users or 'aspirants' due to insufficient data, leading to non-personalized job recommendations, and the global model's bias towards applicants over aspirants.
Innovation Solution
Implementing a cohort-based generalized linear mixed effect model that trains at a medium level of granularity, using cohorts to identify patterns across users with common characteristics, allowing for personalized scoring for applicants and semi-personalized scoring for aspirants, and enabling the use of per-cohort models to address the 'cold-start' problem.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If global ranking algorithms are modified to improve certain queries, then query-specific performance is improved, but other queries become degraded
Solution Approach 1:
The patent segments the user base into distinct cohorts (applicants and aspirants) and trains separate models for each cohort. This segmentation allows the system to optimize ranking algorithms specifically for each group without negative transfer between groups, resolving the contradiction between query-specific performance and generalization across queries.
Solution Approach 2:
The patent applies local quality by creating cohort-specific ranking models that are tailored to the characteristics of each user group. The applicant model and aspirant model have different feature importances and scoring mechanisms optimized for their respective behaviors, allowing high accuracy for each specific cohort while maintaining adaptability through the cohort-based architecture.
2Measurement precision
If text and entity-based features are used for explicit searches, then matching accuracy is improved, but personalization capability deteriorates when explicit queries are not provided
Solution Approach 1:
The patent performs preliminary action by pre-training cohort-specific models using historical data from both explicit and implicit interactions. This allows the system to have pre-computed cohort profiles and behavior patterns ready before implicit searches occur, enabling personalization even when no explicit query is provided. The models learn from past user behavior to make accurate predictions for implicit search scenarios.
3Adaptability or versatility
If per-user models are trained for all users, then personalization is improved, but computational complexity increases for users with insufficient data
Solution Approach 1:
The patent merges users into cohorts based on shared characteristics and behavior patterns. By combining data from multiple users within the same cohort, the system achieves personalization effects without requiring sufficient individual user data. This merging approach reduces computational complexity for individual user modeling while maintaining personalization capability through cohort-level patterns.
Solution Approach 2:
The cohort-based models serve multiple functions: they provide personalization for users with sufficient data, serve as the primary model for users with insufficient data (cold-start problem), and can be combined with global models. This multi-functionality allows a single cohort model to address multiple scenarios, reducing overall system complexity while maintaining adaptability.
Data Source
AI summary
In an example embodiment, cohort-based generalized linear mixed effect model (GLMIX) training is performed to identify patterns across cohorts of users, rather than slicing across all users blindly without accounting for common characteristics of users. Thus, rather than performing GLMIX training at just the finest granular level (e.g., user-level and job-level) or the highest level (global level), a “medium” level of granularity is used to train the GLMIX model at cohort-level.