Contextual Search Ranking via Entity Topic Representations
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Solution Overview
Problem
Conventional search ranking techniques in online networks rely on exact matches, often omitting relevant candidates with semantically similar attributes, and use non-interpretable methods, leading to inefficient job seeking and recruitment processes.
Innovation Solution
The implementation of a contextual search ranking system using entity topic representations, where a topic model generates embeddings to calculate semantic similarity between recruiter search parameters and candidate attributes, allowing for rankings based on contextual matches rather than exact matches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional search ranking techniques use exact matches, then search precision is improved, but relevant candidates with semantically similar attributes are omitted
Solution Approach 1:
The patent transforms the search matching parameter from exact attribute matching to semantic similarity scoring using topic model embeddings. This allows candidates with semantically similar attributes (even without exact matches) to be ranked appropriately, resolving the contradiction between precision and information loss
Solution Approach 2:
The patent introduces topic model embeddings as an intermediary layer between search queries and candidate attributes. This intermediary enables semantic interpretation and comparison, allowing the system to capture relevant candidates that would be missed by direct exact matching while maintaining search precision
2Productivity
If conventional search methods use non-interpretable ranking methods, then computational efficiency is improved, but interpretability of search results deteriorates
Solution Approach 1:
The topic model embeddings serve as an interpretable intermediary that bridges computational efficiency and interpretability. The embeddings provide semantic meaning that can be understood and analyzed, unlike non-interpretable black-box methods, while still enabling efficient computational processing through vector operations
3Measurement precision
If exact attribute matching is used, then search accuracy is improved, but the number of relevant candidates found decreases
Solution Approach 1:
The patent changes the matching parameter from binary exact match to continuous semantic similarity scoring. This allows the system to identify candidates with semantically similar attributes that would be excluded by exact matching, thereby increasing the quantity of relevant candidates while maintaining search accuracy through the semantic interpretation
Data Source
AI summary
The disclosed embodiments provide a system for processing data. During operation, the system obtains a first embedding generated by a topic model from parameters of searches by a first recruiting entity and obtains a set of additional embeddings generated by the topic model from attributes of a set of candidates. Next, the system determines match features that include measures of similarity between the first embedding and each embedding in the set of additional embeddings. The system then applies a machine learning model to the match features and additional features for the candidates to produce a set of scores for the candidates. Finally, the system generates a ranking of the candidates according to the scores and outputs at least a portion of the ranking as search results of a current search by the first recruiting entity.