GLMix Model for Personalized Talent Search Suggestions
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Solution Overview
Problem
Traditional methods for talent search on social networks require manual effort and many trials to translate hiring position criteria into effective search queries, as they need to identify required skills, alternatives, companies, and candidate education backgrounds, and this knowledge becomes outdated quickly.
Innovation Solution
A system utilizing a generalized linear mixed effect model (GLMix) and deep structured semantic models to create a heterogeneous graph structure, allowing for the calculation of similarity scores between entities and providing personalized smart suggestions by mapping non-standardized search terms to standardized entities, thereby enhancing search query efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual search query creation is used, then recruiters can perform talent searches, but the process requires many trials and significant time to obtain satisfactory results
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing similarity scores between job titles and candidate profiles in advance. When a recruiter searches, the system has already prepared embedding vectors and similarity metrics, enabling instant retrieval of relevant candidates without requiring recruiters to manually craft and test multiple queries.
Solution Approach 2:
The patent replaces the mechanical manual process of query creation with an automated machine learning system. The GLMix model and embedding algorithms automatically compute similarity scores and generate search queries based on job descriptions, substituting the recruiter's manual cognitive work with computational processes that operate at machine speed.
2Measurement precision
If standardized entity mapping is implemented, then search accuracy improves, but the system complexity increases due to heterogeneous graph structures and machine learning models
Solution Approach 1:
The patent introduces embedding vectors as intermediary representations between raw text data and the heterogeneous graph structure. These dense vector representations serve as a bridge, allowing the system to handle unstructured job descriptions and standardized entity mappings without directly exposing the complexity of the underlying graph database and ML models to users.
Solution Approach 2:
The system transforms categorical job title data into continuous embedding space parameters. By converting discrete entity labels into continuous vector representations with similarity scores, the system enables nuanced matching while abstracting away the complexity of the heterogeneous graph structure through parameter-based operations.
3Reliability
If manual query refinement is performed, then search results can be optimized, but the knowledge required becomes outdated quickly requiring continuous updates
Solution Approach 1:
The system implements feedback loops where recruiter interactions with search results (clicks, views, applications) are continuously fed back into the ML models. This feedback mechanism allows the system to automatically adapt to changing job market trends and knowledge without requiring manual updates, maintaining reliability while improving adaptability over time.
Solution Approach 2:
The patent creates a dynamic system where the embedding vectors and similarity scores are not static but continuously updated based on new data. The heterogeneous graph structure allows for dynamic addition of new entities and relationships, enabling the system to adapt to evolving job titles, skills, and industry trends automatically.
Data Source
AI summary
In an example, an indication of a plurality of different entities in a social networking service is received, including at least two entities having a different entity type. Then a plurality of user profiles in the social networking service are accessed. A machine-learned model is then used to calculate, based on co-occurrence counts reflecting a number of user profiles in the plurality of user profiles in which corresponding nodes co-occurred, a similarity score between a first node and second node by computing distance between the first node and the second node in a d-dimensional space on which a plurality of entities are mapped, the similarity score generated using a generalized linear mixed model having a global coefficient vector applied to global function pertaining to the co-occurrence counts and a first random effects coefficient vector applied to a random effects per-country function.