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- W2339163939 abstract "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." @default.
- W2339163939 created "2016-06-24" @default.
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- W2339163939 date "2016-04-11" @default.
- W2339163939 modified "2023-09-27" @default.
- W2339163939 title "Towards Mobile Query Auto-Completion" @default.
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- W2339163939 doi "https://doi.org/10.1145/2872427.2882977" @default.
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