Cold Start Candidate Recommendation via Two-Phase Search
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Solution Overview
Problem
Candidate recommendation systems face a 'cold start' issue when deployed in new organizations, where the lack of industry-specific training data leads to suboptimal ranking quality, causing organizations to delay using the system until sufficient data is collected, resulting in less accurate candidate lists.
Innovation Solution
A two-phase search scheme is implemented, using a coarse search query parser to generate candidate vectors and rank candidates based on initial scores, followed by a re-ranking parser that evaluates candidate indices to provide updated scores, employing a cold start query parser to improve ranking quality during the initial training phase.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a candidate recommendation system is deployed in a new organization without industry-specific training data, then the system can be immediately available for use, but the ranking quality of candidate lists becomes suboptimal
Solution Approach 1:
The system performs preliminary actions by collecting candidate data and generating initial rankings during the deployment phase. The coarse search query parser pre-processes candidate information and creates initial rankings that can be immediately provided to users, while simultaneously collecting data for future model training. This allows the system to be useful from day one while preparing for improved performance.
Solution Approach 2:
The system implements self-service by automatically collecting candidate data from initial rankings and using this data to train and improve its own ranking model over time. The system serves itself by converting its own operational data into training material, enabling continuous improvement of ranking quality without external intervention or manual data collection processes.
2Reliability
If organizations wait to collect sufficient training data before deploying the system, then ranking quality can be improved, but the system remains unused during the data collection period
Solution Approach 1:
The system maintains continuous useful action by providing candidate rankings immediately upon deployment using the coarse search query parser. Instead of remaining idle during data collection, the system continuously operates, collects data from its operations, and improves its ranking capabilities while simultaneously serving organizational needs. This eliminates the gap between data collection and system utilization.
Solution Approach 2:
The coarse search query parser acts as an intermediary that provides immediate ranking capability during the transition period before the trained model is ready. It serves as a temporary solution that bridges the gap between system deployment and having sufficient training data, allowing the system to be productive immediately while preparing for long-term high-quality rankings.
3Ease of operation
If a coarse search query parser is used to generate initial candidate rankings without trained models, then the system can operate immediately, but the initial rankings lack precision
Solution Approach 1:
The system implements dynamics by transitioning from a static coarse search query parser to a dynamic trained ranking model over time. Initially, the coarse parser provides basic functionality, but as the system collects data and trains its model, it dynamically improves to provide increasingly accurate rankings. This allows the system to adapt its precision based on the availability of training data while maintaining continuous operation.
Solution Approach 2:
The coarse search query parser performs preliminary ranking actions that provide immediate operational capability. These initial rankings, while not highly precise, serve as a starting point that allows the system to operate immediately. The parser collects data from these initial operations and uses it to train a more precise model, converting preliminary imperfect actions into valuable training material for future improvement.
Data Source
AI summary
A system and method are presented for cold start candidate recommendation. In some examples, a search query request that includes a candidate search parameter can be received for a candidate list. During a first search query, a subset of candidates from a plurality of candidates can be identified based on a comparison of each candidate vector for each candidate and a candidate search parameter vector for the candidate search parameter, and ranked to provide an initial ranked candidate list based on assigned scores for the subset of candidates. During a second search query, the search parameter a candidate index can be evaluated to identify a set of candidates from the plurality of candidates, re-ranked to provide an updated ranked candidate list corresponding to the candidate list based on updated assigned scores for the set candidates and a re-ranking parameter.