A user trajectory graph may be constructed based on spatio-temporal data and mobility pattern extracted from trajectory graph. Users may be clustered into groups, wherein the users in a same group posses similar feature values in the mobility pattern, and the users in different groups have different feature values, to identify personas and location sets. A distribution model may be constructed that models user timing and location preference, wherein an outcome indicates a preference for a particular time bin on a particular day for a particular location.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method to automatically determine user mobility behaviors and interaction preferences using spatio-temporal data and engage users, the method performed by one or more processors, comprising: receiving spatio temporal data comprising location data as a function of time, app interaction data as a function of time, and geographic map data comprising geographic coordinates; constructing a user trajectory graph based on the spatio temporal data; determining mobility pattern based on the user trajectory graph; deriving features from the mobility patterns; clustering users into groups, wherein the users in a same group have similar feature values associated with the features, and the users in different groups have different feature values, whether a feature value is similar or different determined based on meeting a similarity threshold; identifying personas and location sets based on the clustering; constructing a distribution model, wherein an outcome indicates a preference for a particular time bin on a particular day for a particular location; and monitoring user device's location and responsive to detecting that the user device is at the particular location at a particular time estimated as a preferred time and location, pushing content to the user device. 2. The method of claim 1, wherein the trajectory graph comprises nodes representing locations a user has visited and edges representing travel between the nodes, the edges having associated edge weights that indicate likelihood of the travel between the nodes, the nodes each having a node weight indicating frequency of visits to the respective nodes, wherein the trajectory graph is a directed cyclic graph, and the nodes comprise time duration labels. 3. The method of claim 1, wherein the features comprise user location-time signature comprising aggregated time spent at a given location and at time bins of weekday and weekend days. 4. The method of claim 3, wherein the hour bins comprise groups of hours. 5. The method of claim 3, wherein the features further include one or more of: total number of unique locations visited; number and type of unique locations visited in weekday and weekend routines, respectively; stay time at user's home as a percentage of total stay time across all locations; average stay time at user's home; number of visits to user's home; average distance from home to other locations with direct visits; distance from home to work; number and type of the unique locations that are only visited during weekdays; and number and type of the unique locations that are only visited during weekends. 6. The method of claim 3, wherein the features are clustered to determine location types comprising at least home and work. 7. The method of claim 3, wherein the distribution model comprises one or more of a time-preference model indicating a preferred time and location for opening a mobile app. 8. A computer readable storage medium storing a program of instructions executable by a machine to perform a method to automatically determine user mobility behaviors and interaction preferences using spatio-temporal data and engage users, the method comprising: receiving spatio temporal data comprising location data as a function of time, app interaction data as a function of time, and geographic map data comprising geographic coordinates; constructing a user trajectory graph based on the spatio temporal data; determining mobility pattern based on the user trajectory graph; deriving features from the mobility patterns; clustering users into groups, wherein the users in a same group have similar feature values associated with the features, and the users in different groups have different feature values, whether a feature value is similar or different determined based on meeting a similarity threshold; identifying personas and location sets based on the clustering; constructing a distribution model, wherein an outcome indicates a preference for a particular time bin on a particular day for a particular location; and monitoring user device's location and responsive to detecting that the user device is at the particular location at a particular time estimated as a preferred time and location, pushing content to the user device. 9. The computer readable storage medium of claim 8, wherein the trajectory graph comprises nodes representing locations a user has visited and edges representing travel between the nodes, the edges having associated edge weights that indicate likelihood of the travel between the nodes, the nodes each having a node weight indicating frequency of visits to the respective nodes, wherein the trajectory graph is a directed cyclic graph and the nodes comprise time duration labels. 10. The computer readable storage medium of claim 8, wherein the features comprise user location-time signature comprising aggregated time spent at a given location and at time bins of weekday and weekend days. 11. The computer readable storage medium of claim 10, wherein the hour bins comprise groups of hours. 12. The computer readable storage medium of claim 10, wherein the features further include one or more of: total number of unique locations visited; number and type of unique locations visited in weekday and weekend routines, respectively; stay time at user's home as a percentage of total stay time across all locations; average stay time at user's home; number of visits to user's home; average distance from home to other locations with direct visits; distance from home to work; number and type of the unique locations that are only visited during weekdays; and number and type of the unique locations that are only visited during weekends. 13. The computer readable storage medium of claim 10, wherein the features are clustered to determine location types comprising at least home and work. 14. The computer readable storage medium of claim 10, wherein the distribution model comprises one or more of a time-preference model indicating a preferred time and location for opening a mobile app. 15. A system to automatically determine user mobility behaviors and interaction preferences using spatio-temporal data and engage users, comprising: one or more processor communicatively couple to one or more storage devices, one or more of the processors operable to receive spatio temporal data comprising location data as a function of time, app interaction data as a function of time, and geographic map data comprising geographic coordinates from one or more of the storage devices, one or more of the processors further operable to construct a user trajectory graph based on the spatio temporal data, one or more of the processors further operable to determine mobility pattern based on the user trajectory graph, one or more of the processors further operable to derive features from the mobility patterns, one or more of the processors further operable to cluster users into groups, wherein the users in a same group have similar feature values associated with the features, and the users in different groups have different feature values, whether a feature value is similar or different determined based on meeting a similarity threshold, one or more of the processors further operable to identify personas and location sets based on the clustering, one or more of the processors further operable to construct a distribution model, wherein an outcome indicates a preference for a particular time bin on a particular day for a particular location, one or more of the processors further operable to monitor user device's location and responsive to detecting that the user device is at the particular location at a particular time estimated as a preferred time and location, one or more of the processors further operable to push content via a network to the user device. 16. The system of claim 15, wherein the trajectory graph comprises nodes representing locations a user has visited and edges representing travel between the nodes, the edges having associated edge weights that indicate likelihood of the travel between the nodes, the nodes each having a node weight indicating frequency of visits to the respective nodes, wherein the trajectory graph is a directed cyclic graph and wherein the nodes comprise time duration labels. 17. The system of claim 15, wherein the features comprise user location-time signature comprising aggregated time spent at a given location and at time bins of weekday and weekend days. 18. The system of claim 17, wherein the hour bins comprise groups of hours. 19. The system of claim 15, wherein the features are clustered to determine location types comprising at least home and work. 20. The system of claim 15, wherein the distribution model comprises one or more of a time-preference model indicating a preferred time and location for opening a mobile app.