Name
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 e ciency, 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 di erent 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 di erent 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 con rming the e ectiveness 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.
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Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3132847.3132869 | CIKM |
DocType | Volume | ISBN |
Journal | abs/1709.00653 | 978-1-4503-4918-5 |
Citations | PageRank | References |
1 | 0.35 | 19 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Viet Ha-Thuc | 1 | 1 | 0.35 |
| Yan Yan | 2 | 691 | 31.13 |
| Xianren Wu | 3 | 152 | 13.20 |
| Vijay Dialani | 4 | 142 | 12.30 |
| Abhishek Gupta | 5 | 30 | 2.74 |
| Shakti Sinha | 6 | 33 | 3.87 |