From Query-By-Keyword to Query-By-Example: LinkedIn Talent Search Approach
Viet Ha-Thuc, Yan Yan, Xianren Wu, Vijay Dialani, Abhishek Gupta,, Shakti Sinha

TL;DR
This paper introduces a novel Query-By-Example talent search system at LinkedIn, which generates search queries from ideal candidate examples, improving search efficiency and ranking accuracy over traditional keyword-based methods.
Contribution
The paper presents a new Query-By-Example approach for talent search, addressing challenges in query generation, leveraging product logs, and adapting ranking features, with experimental validation.
Findings
Effective query generation from ideal candidates
Improved search ranking performance
Successful deployment to all LinkedIn members
Abstract
One key challenge in talent search is to translate complex criteria of a hiring position into a search query, while it is relatively easy for a searcher to list examples of suitable candidates for a given position. To improve search efficiency, we propose the next generation of talent search at LinkedIn, also referred to as Search By Ideal Candidates. In this system, a searcher provides one or several ideal candidates as the input to hire for a given position. The system then generates a query based on the ideal candidates and uses it to retrieve and rank results. Shifting from the traditional Query-By-Keyword to this new Query-By-Example system poses a number of challenges: How to generate a query that best describes the candidates? When moving to a completely different paradigm, how does one leverage previous product logs to learn ranking models and/or evaluate the new system with no…
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Taxonomy
TopicsInformation Retrieval and Search Behavior · Web Data Mining and Analysis · Data Management and Algorithms
