Abstract: A system coupled to a packet-based network is configured to generate geo-blocks for location-based information services. Each of the geo-blocks corresponds to a geographical region within a geographical area and has at least one border defined by a real-world object. The system is configured to obtain geo data related to real-world objects including transportation routes and natural boundaries in the geographical area, partition the geographical area into initial geo-blocks using the geo data, process a plurality of information requests with respect to at least some of the initial geo-blocks and with respect to initial geo-fences associated with a plurality of points of interests (POIs) to generate entries in one or more databases, and enrich at least some of the initial geo-block with block-level meta data extracted from the entries in the one or more databases to form enriched geo-blocks that are highly indicative of location dependent attributes such as intention and demographics.

Abstract: The present disclosure provides methods and systems that utilize mobile device location events and machine learning and generate predicative classification/regression model for lookalike prediction. Location related features, together with other user level information, are extracted, transformed and used as model feature input, and a client specified list of mobile devices or their associated users are used as prediction target. This system makes efficient use of different types of location events and thus offers improved scale and performance. It also enjoys many benefits offered by a machine learning platform, such as automatic adaptation to different lists of seed lists, addition of new features and changes in data statistical properties.

Abstract: A system includes a machine learning module configured to train a location prediction model using features constructed from mobile device data with time stamps in a training time period, and labels extracted from mobile device data with time stamps in a training time frame. The system further includes a prediction module configured apply the prediction model to a feature set constructed using mobile device data associated with a mobile device with time stamps in a prediction time period to obtain a prediction result corresponding to the mobile device. The system further includes a calibration module configured to obtain a calibration model corresponding to an information campaign, and a calibrated prediction module configured to apply the calibration model to the prediction result to obtain a calibrated probability for the mobile device to have at least one location event at any of one or more locations associated with the information campaign during a prediction time frame.

Abstract: A system for predicting a conversion rate relating to targeted location events for a test campaign includes one or more campaign databases configured to store campaign parameters of a set of historical campaigns. The system further includes a feature engineering module configured to construct a training feature space corresponding to the set of historical campaigns, and to determine a set of labels including a respective conversion rate for each respective historical campaign of the plurality of historical campaigns. The system further includes a model training module configured to machine train a conversion rate prediction model using the training feature space and the set of labels.

Abstract: A system includes a machine learning module configured to train a location prediction model for an information campaign, a front-end server configured to receive and process information requests, and a prediction unit. During the information campaign, the prediction unit is configured to use the location prediction model to predict a conversion probability for any particular mobile device associated with a qualified information request received during any respective time unit. The conversion probability corresponds to a predicted probability of the particular mobile device having at least one location event at any of one or more POIs during a particular time frame. The front-end server is further configured to determine a respective target number of conversions to be achieved by the information campaign during the respective time unit, and to determine a response to the particular information request based at least in part on the conversion probability and on the respective target number of conversions.

Abstract: A system coupled to a packet-based network is configured to predict the locations of one or more mobile devices communicating with the packet-based network. The system comprises a request processor configured to detect location events associated with mobile devices communicating with the packet-based network, each location event corresponding to a time stamp and identifying a geo-place in a geo database. The system further comprises an off-line prediction subsystem configured to train a plurality of off-line prediction models and an on-line prediction model using various historical location events. The off-line prediction subsystem is further configured to generate off-line prediction results corresponding to the off-line prediction models.

Abstract: The present disclosure provides methods and systems that utilize mobile device location events and machine learning and generate predicative classification/regression model for lookalike prediction. Location related features, together with other user level information, are extracted, transformed and used as model feature input, and a client specified list of mobile devices or their associated users are used as prediction target. This system makes efficient use of different types of location events and thus offers improved scale and performance. It also enjoys many benefits offered by a machine learning platform, such as automatic adaptation to different lists of seed lists, addition of new features and changes in data statistical properties.

Abstract: A system coupled to a packet-based network is configured to generate geo-blocks based on geo data related to real-world objects including transportation routes and natural boundaries in a geographical area such that each of the geo-blocks corresponds to a geographical region within the geographical area and has at least one border defined by a real-world object. The system is further configured to determine a performance measure with respect to a point of interest (POI) for each of a set of geo-blocks, and to select one or more geo-blocks each having a performance measure above a threshold from the set of geo-blocks to form a geo-fence for the POI. The threshold is adjustable based on a pacing status of an information campaign associated with the POI.

Abstract: The present disclosure provides methods and systems that utilize mobile device location events and machine learning and generate predicative classification/regression model for lookalike prediction. Location related features, together with other user level information, are extracted, transformed and used as model feature input, and a client specified list of mobile devices or their associated users are used as prediction target. This system makes efficient use of different types of location events and thus offers improved scale and performance. It also enjoys many benefits offered by a machine learning platform, such as automatic adaptation to different lists of seed lists, addition of new features and changes in data statistical properties.

Abstract: A system coupled to a packet-based network is configured to generate geo-blocks based on geo data related to real-world objects including transportation routes and natural boundaries in a geographical area such that each of the geo-blocks corresponds to a geographical region within the geographical area and has at least one border defined by a real-world object. The system is further configured to determine a performance measure with respect to a point of interest (POI) for each of a set of geo-blocks, and to select one or more geo-blocks each having a performance measure above a threshold from the set of geo-blocks to form a geo-fence for the POI. The threshold is adjustable based on a pacing status of an information campaign associated with the POI.

Abstract: A system coupled to a packet-based network is configured to predict the locations of one or more mobile devices communicating with the packet-based network. The system comprises a request processor configured to detect location events associated with mobile devices communicating with the packet-based network, each location event corresponding to a time stamp and identifying a geo-place in a geo database. The system further comprises an off-line prediction subsystem configured to train a plurality of off-line prediction models and an on-line prediction model using various historical location events. The off-line prediction subsystem is further configured to generate off-line prediction results corresponding to the off-line prediction models.

Abstract: A system coupled to a packet-based network is configured to predict the locations of mobile devices that have communicated with the packet-based network. The system includes a request processor configured to detect location events associated with mobile devices communicating with the packet-based network, each location event corresponding to a time stamp and identifying a geo-place in a geo database. The geo-places include geo-blocks and geo-fences. The system further comprises a location prediction subsystem configured to construct first feature space using first location events and second feature space using second location events, and to extract a set of labels from third location events. The location prediction subsystem is further configured to train a prediction model using the first feature space and the set of labels, and to apply the prediction model to the second feature space to obtain prediction results.

Abstract: The present disclosure provides methods and systems that utilize mobile device location events and machine learning and generate predicative classification/regression model for lookalike prediction. Location related features, together with other user level information, are extracted, transformed and used as model feature input, and a client specified list of mobile devices or their associated users are used as prediction target. This system makes efficient use of different types of location events and thus offers improved scale and performance. It also enjoys many benefits offered by a machine learning platform, such as automatic adaptation to different lists of seed lists, addition of new features and changes in data statistical properties.

Abstract: A system coupled to a packet-based network is configured to predict the locations of mobile devices that have communicated with the packet-based network. The system includes a request processor configured to detect location events associated with mobile devices communicating with the packet-based network, each location event corresponding to a time stamp and identifying a geo-place in a geo database. The geo-places include geo-blocks and geo-fences. The system further comprises a location prediction subsystem configured to construct first feature space using first location events and second feature space using second location events, and to extract a set of labels from third location events. The location prediction subsystem is further configured to train a prediction model using the first feature space and the set of labels, and to apply the prediction model to the second feature space to obtain prediction results.

Abstract: System and methods for running a location-based information campaign (campaign) select one or more first geo-blocks to form a first geo-fence from a plurality of geo-blocks each corresponding to a geographical region having at least one border defined by a real-world object and overlapping substantially with a targeted region associated with the location-based information campaign, and process information requests with respect to the first geo-fence. Each of the one or more first geo-blocks is associated with a respective performance score above a first threshold. The system and methods further monitor a pacing status associated with the campaign, and in response to a pacing goal associated with the campaign not being met, define a second geo-fence for the campaign, the second geo-fence including the one or more first geo-blocks and one or more second geo-blocks each associated with a respective performance score above a second threshold that is below the first threshold.

Abstract: A system for processing information requests associated with mobile devices comprises an information server configured to build a search query for an information request based on the location features and other data therein and to search an information database for matching information documents. The matching information documents including information documents having different types of performance measure, including a first document using an impression-based performance measure, a second document using a click/call-based performance measure and a third document using an off-line site-visit-based performance measure. The information server is further configured rank the matching documents based on their respective performance measures and to select a matching document based on their respective rankings.

Abstract: System and methods for running a location-based information campaign (campaign) select one or more first geo-blocks to form a first geo-fence from a plurality of geo-blocks each corresponding to a geographical region having at least one border defined by a real-world object and overlapping substantially with a targeted region associated with the location-based information campaign, and process information requests with respect to the first geo-fence. Each of the one or more first geo-blocks is associated with a respective performance score above a first threshold. The system and methods further monitor a pacing status associated with the campaign, and in response to a pacing goal associated with the campaign not being met, define a second geo-fence for the campaign, the second geo-fence including the one or more first geo-blocks and one or more second geo-blocks each associated with a respective performance score above a second threshold that is below the first threshold.

Abstract: The present disclosure provides a mobile advertising platform in which mobile user locations and other information are translated into indications of mobile user intent to approach certain businesses, and advertisers can fill mobile advertising requests or choose to price their bids for mobile supplies based on such indications. In certain embodiments, pre-defined places associated with business/brand names are created, and mobile advertising requests are processed to determine if the associated with mobile devices have triggered any of these pre-defined places. If a mobile advertising request is determined to have triggered one or more of the pre-defined places, it is annotated with the triggered place(s), and advertisements are selected based on the triggered places and other factors. The annotated requests with the triggered places can also be commodities in a location market place, which are auctioned to the mobile advertisers, who can place their bids on the triggered places.