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Learning to Rank Personalized Search Results in Professional Networks
May 16, 2016 Β· Declared Dead Β· π Annual International ACM SIGIR Conference on Research and Development in Information Retrieval
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Authors
Viet Ha-Thuc, Shakti Sinha
arXiv ID
1605.04624
Category
cs.IR: Information Retrieval
Cross-listed
cs.LG
Citations
7
Venue
Annual International ACM SIGIR Conference on Research and Development in Information Retrieval
Last Checked
4 months ago
Abstract
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.
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