Learning to Rank Personalized Search Results in Professional Networks
This addresses the problem of delivering relevant search results for users in professional networks like LinkedIn, but it appears incremental as it builds on existing learning-to-rank methods with new data sources.
The paper tackles the challenge of personalizing search results in professional networks by inferring user intents and using homophily to capture searcher-result similarities, applying learning-to-rank to combine these with standard features.
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.