  LinkedIn is the largest professional network with more than 350 million
members. As the member base increases, searching for experts becomes more and
more challenging. In this paper, we propose an approach to address the problem
of personalized expertise search on LinkedIn, particularly for exploratory
search queries containing {\it skills}. In the offline phase, we introduce a
collaborative filtering approach based on matrix factorization. Our approach
estimates expertise scores for both the skills that members list on their
profiles as well as the skills they are likely to have but do not explicitly
list. In the online phase (at query time) we use expertise scores on these
skills as a feature in combination with other features to rank the results. To
learn the personalized ranking function, we propose a heuristic to extract
training data from search logs while handling position and sample selection
biases. We tested our models on two products - LinkedIn homepage and LinkedIn
recruiter. A/B tests showed significant improvements in click through rates -
31% for CTR@1 for recruiter (18% for homepage) as well as downstream messages
sent from search - 37% for recruiter (20% for homepage). As of writing this
paper, these models serve nearly all live traffic for skills search on LinkedIn
homepage as well as LinkedIn recruiter.
