From Query-By-Keyword to Query-By-Example: LinkedIn Talent Search Approach
This addresses the problem of inefficient talent search for recruiters and hiring managers on LinkedIn by shifting from keyword-based to example-based queries, representing a novel method for a known bottleneck.
The paper tackles the challenge of translating complex hiring criteria into search queries by proposing a Query-By-Example system for LinkedIn Talent Search, where searchers provide ideal candidates as input to generate queries and retrieve results, with experimental results confirming its effectiveness on query building and ranking tasks.
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 existing usage logs? Finally, given the different nature between the two search paradigms, the ranking features typically used for Query-By-Keyword systems might not be optimal for Query-By-Example. This paper describes our approach to solving these challenges. We present experimental results confirming the effectiveness of the proposed solution, particularly on query building and search ranking tasks. As of writing this paper, the new system has been available to all LinkedIn members.