Protecting User Privacy by Monitoring API Queries

This publication describes a method for protecting user privacy from targeted application interface program (API) queries made to a prediction service on a computing device. More specifically, the method involves utilizing an API Query Manager to monitor API calls made by applications to the prediction service. If the API Query Manager determines a lack of positive feedback (e.g., screen content presented on a display of the computing device does not match the personalized prediction returned from an API call), then the API Query Manager can throttle further API calls made by the application to protect user privacy

SUGGESTING LOCAL ACTIVITIES BY INFERRED CONTEXT

A system for suggesting local activities based on inferred user and location attributes (e.g. familiarity with an area, time of day, day of week), as well as explicitly stated attributes (e.g. group size + composition, such as alone, couple, with friends, with kids) is presented. The system is based on an ontology of activities including highlevel intents, moods etc. afforded by a particular locality. These activities are then mapped to contextual factors by expert editors who assign a value to each activity and contextual factor intersection, indicating the degree to which an activity is suited for a particular context. The system then suggests activities based on the inferred and explicit userstated contextual factors using the mapping. Advantages of the system include generation of expert suggestions or recommendations that are similar to what people provide one another, which encourages discovery by highlighting locally typical activities