Tell me where to go and what to do next, but do not bother me

In this demonstration, we present a system that recommends to the user the locations and activities she/he might be interested in according to history GPS trajectories and public places of interest (POI) data. Its innovation lies in the acceptable performance of recommendations in cases where no user comments on activity types are available. Such situations are more realistic considering the restrictions on mobile devices' abilities, users' privacies, or business secret. For this purpose, we first extract stay points according to uses' trajectories, and label them with the top-k common activities which have the most possibility in terms of the POI dataset. Then, by taking stay points as observations, and activities as hidden states, a Hidden Markov model is built to learn the transfer possibilities between activities and the generation probabilities between activities and stay points. Finally, with the obtained model, our system can perform two types of recommendation, i.e. the history based recommendation and the similarity based recommendation. The results of former type are those stay points from user's own history positions. While, the latter one conducts collaborative filtering by taking history based recommendation results from similar users. The demonstration shows the running effects of the implemented prototype system, in which the Microsoft GeoLife trajectories dataset and the "DianPing.com" POI dataset were loaded. The preliminary experimental results demonstrate the feasibility.