Deep Natural Language Processing for Search and Recommender Systems

被引：15

作者：

Guo, Weiwei ^{[1
]}

Gao, Huiji ^{[1
]}

Shi, Jun ^{[1
]}

Long, Bo ^{[1
]}

Zhang, Liang ^{[1
]}

Chen, Bee-Chung ^{[1
]}

Agarwal, Deepak ^{[1
]}

机构：

[1] LinkedIn, Mountain View, CA 94043 USA

来源：

KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING | 2019年

关键词：

Deep Learning; Natural Language Understanding/Generation; Search Engine; Recommender System;

D O I：

10.1145/3292500.3332290

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Search and recommender systems share many fundamental components including language understanding, retrieval and ranking, and language generation. Building powerful search and recommender systems requires processing natural language effectively and efficiently. Recent rapid growth of deep learning technologies has presented both opportunities and challenges in this area. This tutorial offers an overview of deep learning based natural language processing (NLP) for search and recommender systems from an industry perspective. It first introduces deep learning based NLP technologies, including language understanding and language generation. Then it details how those technologies can be applied to common tasks in search and recommender systems, including query and document understanding, retrieval and ranking, and language generation. Applications in LinkedIn production systems are presented. The tutorial concludes with discussion of future trend.

引用

页码：3199 / 3200

页数：2