Nonlinear Search Models for Candidate Relevance
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Solution Overview
Problem
Conventional candidate search systems in online social networking are limited in scope, as they only consider individual characteristics and lack the ability to infer relationships between them, leading to inefficient searches and potentially irrelevant results.
Innovation Solution
The implementation of nonlinear models, such as search trees, that consider empirically determined relationships between content, contextual, and inferred characteristics, allowing for more relevant search results while reducing computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional linear search methods are used to match candidate characteristics with job requirements, then the search process is simple and fast, but the search results lack relevance and accuracy
Solution Approach 1:
The patent transforms the linear search approach into a nonlinear tree-based model, fundamentally changing the search parameter structure. The system evaluates candidates through multiple hierarchical levels (initial filtering, intermediate evaluation, final ranking) rather than simple linear matching, thereby improving search result relevance while managing complexity through structured organization
Solution Approach 2:
The search process is divided into distinct segments or phases represented as tree nodes. Each node performs a specific evaluation function (e.g., skill matching, experience verification, cultural fit assessment), allowing the system to break down the complex search task into manageable segments that collectively improve overall search accuracy
2Measurement precision
If all candidate characteristics are evaluated in detail to ensure comprehensive assessment, then the accuracy of candidate selection improves, but the computational resources and time required increase significantly
Solution Approach 1:
The tree-based search model performs preliminary filtering at upper levels of the tree, eliminating clearly unsuitable candidates before they reach deeper evaluation nodes. This preliminary action reduces the number of candidates requiring detailed assessment, thereby maintaining assessment accuracy for promising candidates while reducing overall processing time and computational resources
Solution Approach 2:
The system applies partial evaluation to candidates based on their progression through the tree structure. Not all evaluation nodes are accessed for every candidate - only those relevant to the candidate's profile and the job requirements. This partial action approach maintains necessary assessment accuracy while avoiding unnecessary computational expenditure on already-disqualified candidates
Data Source
AI summary
A system and method includes receiving a search query and obtaining, from a database, member data of a member. For each of a plurality of nonlinear models, the nonlinear model is traversed based on a comparison of characteristics to conditions to obtain a score, wherein, among the nonlinear models, at least one characteristic is an inferred characteristic based on at least one of: activities by the member in an online networking system; and connections by the member in the online networking system. The score obtained from each of the nonlinear models is combined to obtain a combined score and a user interface to displays information related to the member based, at least in part on the combined score.