Towards Mobile Query Auto-Completion: An Efficient Mobile Application-Aware Approach

被引：12

作者：

Zhang, Aston ^{[1
,2
]}

Goyal, Amit ^{[2
]}

Baeza-Yates, Ricardo ^{[2
]}

Chang, Yi ^{[2
]}

Han, Jiawei ^{[1
]}

Gunter, Carl A. ^{[1
]}

Deng, Hongbo ^{[2
]}

机构：

[1] Univ Illinois, Urbana, IL 61801 USA

[2] Yahoo Labs, Sunnyvale, CA USA

来源：

PROCEEDINGS OF THE 25TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'16) | 2016年

关键词：

Query Auto-Completion; Mobile Application; Mobile Device;

D O I：

10.1145/2872427.2882977

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

We study the new mobile query auto-completion (QAC) problem to exploit mobile devices' exclusive signals, such as those related to mobile applications (apps). We propose AppAware, a novel QAC model using installed app and recently opened app signals to suggest queries for matching input prefixes on mobile devices. To overcome the challenge of noisy and voluminous signals, AppAware optimizes composite objectives with a lighter processing cost at a linear rate of convergence. We conduct experiments on a large commercial data set of mobile queries and apps. Installed app and recently opened app signals consistently and significantly boost the accuracy of various baseline QAC models on mobile devices.

引用

页码：579 / 590

页数：12

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