On-Device Query Auto-Completion for Email Search

Traditional query auto-completion (QAC) relies heavily on search logs collected over many users. However, in on-device email search, the scarcity of logs and the governing privacy constraints make QAC a challenging task. In this work, we propose an on-device QAC method that runs directly on users’ devices, where users’ sensitive data and interaction logs are not collected, shared, or aggregated through web services. This method retrieves candidates from pseudo relevance feedback, and ranks them based on relevance signals that explore the textual and structural information from users’ emails. We also propose a private corpora based evaluation method, and empirically demonstrate the effectiveness of our proposed method.