Temporal Professional Profile Similarity Analysis
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Solution Overview
Problem
Current online professional networking systems lack an effective method to identify similar professional profiles based on temporal career data, which is crucial for career trajectory analysis and recommendation.
Innovation Solution
A system that models professional profiles as sequences of positions over time, generates data structures from these profiles, and calculates a similarity score by aligning and comparing temporal node sequences, incorporating weighting factors and employment transition data to identify similar profiles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If professional profiles are analyzed using traditional static methods, then the analysis is simple to perform, but the accuracy of identifying similar profiles is insufficient
Solution Approach 1:
The patent segments professional profiles into temporal sequences of positions, where each position is represented as a node with attributes (company, title, location, duration). This segmentation allows the system to capture career evolution over time while maintaining manageable complexity through structured data representation.
Solution Approach 2:
The patent introduces a temporal dimension to profile analysis by representing careers as ordered sequences of positions over time. This transforms static profile data into dynamic temporal trajectories, enabling more accurate similarity identification through time-aware comparison methods.
2Measurement precision
If temporal career data is incorporated into profile analysis, then the accuracy of career trajectory analysis is improved, but the computational complexity increases
Solution Approach 1:
The patent performs preliminary actions by pre-processing professional profiles into standardized temporal node sequences before comparison. This includes extracting position data, assigning temporal attributes, and structuring careers as ordered sequences, which reduces computational complexity during the actual similarity calculation phase.
Solution Approach 2:
The patent changes parameters by introducing temporal weights and similarity metrics that account for the timing and duration of positions. This allows the system to prioritize recent experience and account for career progression patterns, improving analysis accuracy while using configurable parameters to control computational intensity.
3Measurement precision
If detailed temporal node sequences are generated and compared, then the precision of profile similarity measurement is enhanced, but the processing time increases
Solution Approach 1:
The patent applies partial action by focusing comparison on key temporal nodes and significant career transitions rather than every detail of every position. The alignment algorithm identifies and weights important comparative points, achieving high precision without processing every possible data point in full detail.
Solution Approach 2:
The patent substitutes mechanical brute-force comparison with an alignment-based algorithm that efficiently matches temporal sequences. This algorithmic approach dynamically aligns node sequences, accounting for temporal offsets and varying career paces, significantly reducing processing time while maintaining precision.
Data Source
AI summary
A system and method for temporal-based professional similarity are provided. In example embodiments, a request to identify, from among a plurality of member profiles of a social network service, a profile that is similar to a source profile, is received. Profile data of the source profile and a candidate profile are accessed from the social network service. Profile features are extracted from the profile data. The profile features include source features extracted from the profile data of the source profile and candidate features extracted from the profile data of the candidate profile. Respective profile features correspond to temporal data included in the profile data. Data structures are generated by structuring the profile features according to the temporal data. The data structures include a source data structure generated using the source features and a candidate data structure generated using the candidate features. A profile similarity score is determined by comparing the candidate data structure with the source data structure. The profile similarity score indicates the similarity between the candidate profile and the source profile.