10 citations · 10 across the 2 of their papers we have counts for
5 papers
AutoField: Automating Feature Selection in Deep Recommender Systems
Yejing Wang, Xiangyu Zhao, Tong Xu +1
Feature quality has an impactful effect on recommendation performance. Thereby, feature selection is a critical process in developing deep learning-based recommender systems. Most…
Talent Search and Recommendation Systems at LinkedIn: Practical Challenges and Lessons Learned
Sahin Cem Geyik, Qi Guo, Bo Hu +4
LinkedIn Talent Solutions business contributes to around 65% of LinkedIn's annual revenue, and provides tools for job providers to reach out to potential candidates and for job see…
Towards Deep and Representation Learning for Talent Search at LinkedIn
Rohan Ramanath, Hakan Inan, Gungor Polatkan +6
Talent search and recommendation systems at LinkedIn strive to match the potential candidates to the hiring needs of a recruiter or a hiring manager expressed in terms of a search…
From Query-By-Keyword to Query-By-Example: LinkedIn Talent Search Approach
Viet Ha-Thuc, Yan Yan, Xianren Wu +3
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 suitabl…
Search by Ideal Candidates: Next Generation of Talent Search at LinkedIn
Viet Ha-Thuc, Ye Xu, Satya Pradeep Kanduri +5
One key challenge in talent search is how to translate complex criteria of a hiring position into a search query. This typically requires deep knowledge on which skills are typical…