Personalised maps in multimodal mobile GIS

Information overload is a critical problem in mobile GIS where user interactions are constrained by device limitations. Identifying relevant map content and generating personalised map versions, tailored towards users' preferences, can improve download times as well as perception on small screens. Whereas some existing applications propose solutions that require explicit user input, we adopt an implicit profiling approach that does not demand off-task input. Modelling preferences in this manner allows us to recommend personalised context-aware spatial content to users whenever they request maps. Providing a multimodal interface further improves a user's mobile geospatial experience as each user has the ability to freely switch between different input modalities, including speech and gesture, depending on which mode best suits their current task and environment. This article provides a description and evaluation of our approach as implemented in CoMPASS, a multimodal mobile GIS that implicitly records user movements and interactions to infer persistent spatial preferences and recommend relevant map content.