Learning to Rank Personalized Search Results in Professional Networks

被引：5

作者：

Ha-Thuc, Viet ^{[1
]}

Sinha, Shakti ^{[1
]}

机构：

[1] LinkedIn, 2029 Stierlin Court, Mountain View, CA 2029 USA

来源：

SIGIR'16: PROCEEDINGS OF THE 39TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL | 2016年

关键词：

Learning-to-Rank; Personalization; Federation;

D O I：

10.1145/2911451.2927018

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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 is applied to combine these signals with standard search features.

引用

页码：461 / 462

页数：2