Personalized expertise search at LinkedIn

@article{HaThuc2015PersonalizedES,
  title={Personalized expertise search at LinkedIn},
  author={Viet Ha-Thuc and Ganesh Venkataraman and Mario Rodriguez and Shakti Sinha and Senthil Sundaram and Lin Guo},
  journal={2015 IEEE International Conference on Big Data (Big Data)},
  year={2015},
  pages={1238-1247}
}
Linkedln 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 skills. In the offline phase, we introduce a collaborative filtering approach based on matrix factorization. Our approach estimates expertise scores for both the skills… CONTINUE READING
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References

Publications referenced by this paper.
Showing 1-10 of 32 references

Collaborative Filtering for Implicit Feedback Datasets

2008 Eighth IEEE International Conference on Data Mining • 2008
View 4 Excerpts
Highly Influenced

Effects of position bias on click-based recommender evaluation

K. Hofmann, A. Schuth, A. Bellogı́n, M. de Rijke
Proceedings of 36th ECIR, 2014, pp. 624–630. • 2014
View 1 Excerpt

Expertise retrieval.

K. Balog, Y. Fang, M. de Rijke, P. Serdyukov, L. Si
Foundations and Trends in Information Retrieval, • 2012
View 1 Excerpt

A Short Introduction to Learning to Rank

IEICE Transactions • 2011
View 1 Excerpt

Future directions in learning to rank

Yahoo! Learning to Rank Challenge • 2011
View 1 Excerpt

Yahoo! Learning to Rank Challenge Overview

Yahoo! Learning to Rank Challenge • 2011
View 2 Excerpts

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