arXiv Open Access 2016

Learning to Rank Personalized Search Results in Professional Networks

Viet Ha-Thuc Shakti Sinha

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Abstrak

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.

Topik & Kata Kunci

cs.IR cs.LG

Penulis (2)

Viet Ha-Thuc

Shakti Sinha

Format Sitasi

APA MLA BibTeX

Ha-Thuc, V., Sinha, S. (2016). Learning to Rank Personalized Search Results in Professional Networks. https://arxiv.org/abs/1605.04624

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Informasi Jurnal

Tahun Terbit: 2016
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓