Groupon, Inc.

Bytedance Inc.

Emma Sawin

Boris Lerner

Amber Roy Chowdhury

Joaquin Delgado

Roger Henry Castillo

Abhaya Parthy

Daniel M Roarty

Alvaro Viloria

Sunil Ramnik Raiyani

Changjian Zou

Jikai Lei

Ramesh Maddula

Raju Balakrishnan

Leopold Silberstein

Computing systems, computing apparatuses, computing methods, and computer program products are disclosed for machine learning ranking. An example computing method includes receiving a search query and determining a plurality of machine learning model execution engines based on the search query and a plurality of search result types. The example computing method further includes generating a plurality of subsets of search results based on the search query and the plurality of machine learning model execution engines. The example computing method further includes generating a set of search results comprising at least one search result from each of the plurality of subsets of search results.

Machine learning systems architectures for ranking

The present disclosure relates to inferring device rendered object interaction behavior. An apparatus is configured to receive historical device rendered object interaction data associated with a client device. The historical device rendered object interaction data includes a plurality of records. Each record is associated with a device rendered object category. For each record, the device rendered object category is extracted. For each device rendered object category, a penalty value is generated. The penalty value is a programmatically generated predicted network time delay during which device rendered objects associated with the device rendered object category are not to be transmitted to the client device. A penalty decision, used to determine whether to apply the penalty value to future transmissions of device rendered objects to the client device, is generated.

Method, apparatus, and computer program product for inferring device rendered object interaction behavior

An apparatus, method, and computer program product are provided to filter and modify option data objects and weighted values associated with option data objects through the application of specific rule sets based on the relative density of option data objects within a particularized area. In some example implementations, option data objects and related parameters are parsed to identify locations associated with the option data object and a weighted value, such as a weighted value generated by a predictive model. Based at least in part on the location associated with the option data object, a determined location of a user of a mobile device, and location-specific distance criteria, the weighted value associated with the option data object may be modified to reflect distance-related option election probabilities.

Apparatus and method for distance-based option data object filtering and modification

A method, computer program product, and apparatus for determining improved data objects are provided. An example method receives a request for a renderable data object from a location source device associated with a user profile including instant location data. The method queries an explicit locations database and an implicit locations database and coranks this multisource locations data. The method compares the coranked locations with one or more object redemption locations of each renderable data object. The method determines a proximity score for each renderable data object and ranks each renderable data object based upon proximity score. The method then identifies a proximate data object having a minimum proximity score and transmits the proximate data object to the location source device.

Clustering and coranking of multi-source location data

The present disclosure relates to inferring device rendered object interaction behavior. An apparatus is configured to receive historical device rendered object interaction data associated with a client device. The historical device rendered object interaction data includes a plurality of records. Each record is associated with an device rendered object category. For each record, the device rendered object category is extracted. For each device rendered object category, a penalty value is generated. The penalty value is a programmatically generated predicted network time delay during which device rendered objects associated with the device rendered object category are not to be transmitted to the client device. A penalty decision, used to determine whether to apply the penalty value to future transmissions of device rendered objects to the client device, is generated.

Growing community of inventors

Palo Alto, CA, United States of America

Emma Sawin