Job Posting Targeting via Implicit Facet Mapping
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Solution Overview
Problem
Conventional online job hosting services struggle to accurately provide information on the expected target audience and job applications for promoted job postings, as existing selection and ranking algorithms are opaque and fail to differentiate between organic and promoted job postings, leading to suboptimal advertising and forecasting.
Innovation Solution
A data processing framework that utilizes a member profile-to-job posting graph with implicit facets, derived from historical data on confirmed hires, to map member profiles to relevant job postings, providing insights into the qualified candidate pool and expected target audience, and incorporating inventory forecasting to estimate job applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional selection and ranking algorithms are used for job postings, then job postings can be presented to members, but the algorithms are opaque and fail to differentiate between organic and promoted job postings, leading to inaccurate information on expected target audience and job applications
Solution Approach 1:
The patent segments the job posting matching framework into separate processing paths for organic job postings and promoted job postings. The selection and ranking algorithms are divided such that organic postings use one set of algorithms while promoted postings use another, more sophisticated set that provides transparent targeting information. This segmentation allows accurate prediction of target audience for promoted postings without requiring the entire system to be overly complex.
Solution Approach 2:
The patent introduces an intermediary layer between the job posting data and the member matching process. This intermediary comprises the separate selection and ranking algorithms that process promoted job postings differently, generating intermediate results including expected target audience information and job application forecasts before final presentation to members. This intermediary structure enables accurate measurements without directly complicating the core matching framework.
2Reliability
If paid job postings receive beneficial processing and prominent positioning, then advertising effectiveness is improved, but the lack of transparent information on target audience and job applications makes it difficult for employers to make informed decisions
Solution Approach 1:
The patent implements feedback mechanisms where the separate selection and ranking algorithms for promoted job postings generate and return information about expected target audience characteristics and projected job application volumes to the employer. This feedback loop allows employers to see the anticipated results of their advertising investment before committing, enabling informed decisions about paid job posting placements and budget allocation.
Solution Approach 2:
The patent performs preliminary calculations and analyses of expected target audience and job application forecasts before the employer finalizes the promoted job posting. The selection and ranking algorithms pre-compute these metrics based on historical data and member profiles, providing advance information that allows employers to make informed decisions about their advertising investment without waiting for actual campaign results.
3Productivity
If the same matching algorithm is used for both organic and promoted job postings, then system simplicity is maintained, but the ability to provide differentiated and accurate information for promoted postings is lost
Solution Approach 1:
The patent segments the processing workflow to handle organic and promoted job postings through different algorithmic paths. While both paths maintain efficient processing, the promoted job posting path incorporates additional selection and ranking algorithms that specifically compute target audience predictions and application forecasts. This segmentation preserves overall system productivity while enhancing measurement precision for promoted postings where it is most needed.
Solution Approach 2:
The patent applies local quality by enhancing the matching algorithm specifically for promoted job postings rather than making the entire system uniformly complex. The improved selection and ranking algorithms with transparent targeting capabilities are applied locally to promoted postings, while organic postings continue to use the simpler, more efficient algorithm. This localized enhancement maintains overall system productivity while achieving accurate job application forecasting where required.
Data Source
AI summary
Described herein is a technique for generating data for a computer system. An apparatus comprises a high-speed communication bus, a memory unit communicatively coupled to the high-speed communication bus, an integrated circuit communicatively coupled to the high-speed communication bus, the integrated circuit to execute a machine learned model trained to receive input data and generate a set of implicit keywords from the input data to support a network service, and an implicit facet mapper to map the input data to an implicit facet of a knowledge graph, a database communicatively coupled to the high-speed communication bus, the database to store the set of implicit keywords and implicit facet in a data structure, and a network interface communicatively coupled to the high-speed communication bus to access a wireless network. Other embodiments are described and claimed.