# Learning to Rank Personalized Search Results in Professional Networks

arXiv: https://arxiv.org/abs/1605.04624 · HTML: https://mlsanity.com/paper/1605.04624/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 May 2016.

## Built on by (most cited first, 2 of 2)

- [Bridging Personalization and Control in Scientific Personalized Search](https://mlsanity.com/paper/2411.02790/) (2024)
- [Personalized and Aspiration-Oriented Career Path Recommendation](https://mlsanity.com/paper/2608.22056/) (2026)

## Abstract (arXiv)

LinkedIn search is deeply personalized - for the same queries, different searchers expect completely different results. This paper presents our approach to achieving this by mining various data sources available in LinkedIn to infer searchers' intents (such as hiring, job seeking, etc.), as well as extending the concept of homophily to capture the searcher-result similarities on many aspects. Then, learning-to-rank (LTR) is applied to combine these signals with standard search features.