AI

Semantic Location in Email Query Suggestion

Abstract

Mobile devices are pervasive, which means that users have access to web content and their personal documents at all locations, not just their home or office. Existing work has studied how locations can influence information needs, focusing on web queries. We explore whether or not location information can be helpful to users who are searching their own personal documents.

We wish to study whether a users’ location can predict their queries over their own personal data, so we focus on the task of query suggestion. While we find that using location directly can be helpful, it does not generalize well to novel locations. To improve this situation, we explore using semantic location: that is, rather than memorizing location-query associations, we generalize our location information to names of the closest point of interest. By using short, semantic descriptions of locations, we find that we can more robustly improve query completion and observe that users are already using locations to extend their own queries in this domain.

We present a simple but effective model that can use location to predict queries for a user even before they type anything into a search box, and which learns effectively even when not all queries have location information.