METHOD FOR PERFORMING DEEP SIMILARITY MODELLING ON CLIENT DATA TO DERIVE BEHAVIORAL ATTRIBUTES AT AN ENTITY LEVEL
A method for performing a deep similarity modeling on client data to derive behavioral attributes at an entity level is provided. The method includes (i) obtaining a first dataset of a first set of entities; (ii) obtaining a second dataset of a second set of entities; (iii) matching identifiers of the first dataset with the second dataset to obtain a matched set of entities; (iv) generating ground truth labels for the matched set of entities; (v) determining a feature combination of at least one generic feature from the first dataset and at least one custom feature from the second dataset for the matched set of entities; (vi) training a deep similarity model using ground truth labels and feature combination as training data to obtain a trained deep similarity model; (vii) determining similar entities from the second dataset using the trained deep similarity model and the classification method.
1 . A processor-implemented method for determining, at a server, using a deep similarity model, a cluster of device identifiers associated with entity devices of entities having attributes that are similar to high confident entities based on location data streams obtained from the entity devices, the method comprising:
obtaining, at the server, a first dataset of a first set of entities that are users associated with a client, wherein the first dataset comprises any of mobile entity identifiers, locations, or hashed email addresses of the users;
obtaining, at the server, a second dataset of a second set of entities from the entity devices in a geographical area, wherein the second dataset comprises the location data streams comprising any of device attributes, connection attributes, and user agent strings
obtaining, at the server, ground truth labels based on the high confident entities from the first dataset;
training a deep similarity model based on the ground truth labels and at least one custom feature specific to the client to obtain a trained deep similarity model; and
determining, at the server, using the trained deep similarity model and a classification method on the second dataset, the cluster of the device identifiers associated with the entity devices of the entities having the attributes that are similar to the high confident entities from the first dataset, from the second dataset.
2 . The processor-implemented method of claim 1 , further comprising
determining, using the trained deep similarity model and one-class classification method on the second dataset, the cluster of the device identifiers associated with the entity devices of the entities having the attributes that are similar to the high confident entities from the first dataset, from the second dataset, wherein the device identifiers are obtained when a plurality of first behavioral attributes of a matched set of entities are similar to a plurality of second behavioral attributes of the second set of entities while comparing each other.
3 . The processor-implemented method of claim 1 , further comprising
determining, using the trained deep similarity model and a binary-class classification method, the cluster of the device identifiers associated with the entity devices of the entities having a combination of the attributes that are similar to the high confident entities from the first dataset and the attributes that are contrary to the high confident entities from the first dataset, from the second dataset, wherein the entities with the attributes that are contrary to the high confident entities from the first dataset comprise a first entity from a matched set of entities and a second entity from the second set of entities, wherein at least one attribute of the first entity is mutually exclusive from at least one attribute of the second entity.
4 . The processor-implemented method of claim 3 , further comprising merging a first behavioral attribute and a second behavioral attribute of the matched set of entities using the ground truth labels, wherein the first behavioral attribute and the second behavioral attribute are associated with two mutually exclusive classes of behavior.
5 . The processor-implemented method of claim 1 , further comprising
determining, using the trained deep similarity model and a multi-class classification method, the cluster of the device identifiers having multiple overlapping attributes of behavior to the high confident entities from the first dataset, from the second dataset, wherein the device identifiers having the multiple overlapping attributes of behavior to the high confident entities from the first dataset, are obtained when a plurality of first behavioral attributes of a matched set of entities overlap in comparison with a plurality of second behavioral attributes of the second set of entities.
6 . The processor-implemented method of claim 5 , further comprising scoring the matched set of entities against a behavioral attribute by:
generating a user scoring model based on a function of the behavioral attributes of the matched set of entities; and
assigning, using the user scoring model, a score for each of the matched set of entities against the behavioral attribute.
7 . The processor-implemented method of claim 1 , further comprising:
obtaining weights of a plurality of behavioral attributes from the client;
configuring the trained deep similarity model based on the weights to obtain a re-configured model; and
generating a cluster for a matched set of entities using the re-configured deep similarity model.
8 . The processor-implemented method of claim 1 , wherein the classification method depends on a level of similarity between behavioral attributes of a matched set of entities and behavioral attributes of the second set of entities.
9 . A system for determining, at a server, using a deep similarity model, a cluster of device identifiers associated with entity devices of entities having attributes that are similar to high confident entities based on location data streams obtained from the entity devices, the system comprising:
a processor; and
a memory that stores a set of instructions, which when executed by the processor, causes it to perform:
obtaining, at the server, a first dataset of a first set of entities that are users associated with a client, wherein the first dataset comprises any of mobile entity identifiers, locations, or hashed email addresses of the users;
obtaining, at the server, a second dataset of a second set of entities from the entity devices in a geographical area, wherein the second dataset comprises the location data streams comprising any of device attributes, connection attributes, and user agent strings
obtaining, at the server, ground truth labels based on the high confident entities from the first dataset;
training a deep similarity model based on the ground truth labels and at least one custom feature specific to the client to obtain a trained deep similarity model, wherein the trained deep similarity model determines attributes associated with the high confident entities from the first dataset; and
determining, at the server, using the trained deep similarity model and a classification method on the second dataset, the cluster of the device identifiers associated with the entity devices of the entities having the attributes that are similar to the high confident entities from the first dataset, from the second dataset.
10 . The system of claim 9 , wherein the processor further performs
determining, using the trained deep similarity model and one-class classification method on the second dataset, the cluster of the device identifiers associated with the entity devices of the entities having the attributes that are similar to the high confident entities from the first dataset, from the second dataset, wherein the device identifiers are obtained when a plurality of first behavioral attributes of a matched set of entities are similar to a plurality of second behavioral attributes of the second set of entities while comparing each other.
11 . The system of claim 9 , wherein the processor further performs entities;
determining, using the trained deep similarity model and a binary-class classification method, the cluster of the device identifiers associated with the entity devices of the entities having a combination of the attributes that are similar to the high confident entities from the first dataset and the attributes that are contrary to the high confident entities from the first dataset, from the second dataset, wherein the entities with the attributes that are contrary to the high confident entities from the first dataset comprise a first entity from a matched set of entities and a second entity from the second set of entities, wherein at least one attribute of the first entity is mutually exclusive from at least one attribute of the second entity.
12 . The system of claim 11 , wherein the processor further performs
merging a first behavioral attribute and a second behavioral attribute of the matched set of entities using the ground truth labels, wherein the first behavioral attribute and the second behavioral attribute are associated with two mutually exclusive classes of behavior.
13 . The system of claim 9 , wherein the processor further performs
determining, using the trained deep similarity model and a multi-class classification method, the cluster of the device identifiers having multiple overlapping attributes of behavior to the high confident entities from the first dataset, from the second dataset, wherein the device identifiers having the multiple overlapping attributes of behavior to the high confident entities from the first dataset, are obtained when a plurality of first behavioral attributes of a matched set of entities overlap in comparison with a plurality of second behavioral attributes of the second set of entities.
14 . The system of claim 9 , wherein the processor further performs scoring a matched set of entities against a behavioral attribute by:
generating a user scoring model based on a function of behavioral attributes of the matched set of entities; and
assigning, using the user scoring model, a score for each of the matched set of entities against the behavioral attribute.
15 . The system of claim 9 , wherein the processor further performs:
obtaining weights of a plurality of behavioral attributes from the client;
configuring the trained deep similarity model based on the weights to obtain a re-configured deep similarity model; and
generating a cluster for a matched set of entities using the re-configured deep similarity model.
16 . The system of claim 9 , wherein the classification method depends on a level of similarity between behavioral attributes of a matched set of entities and behavioral attributes of the second set of entities.
17 . A non-transitory computer readable storage medium storing a sequence of instructions, which when executed by a processor, causes determining, at a server, using a deep similarity model, a cluster of device identifiers associated with entity devices of entities having attributes that are similar to the high confident entities based on location data streams obtained from the entity devices, the sequence of instructions comprising:
obtaining, at the server, a first dataset of a first set of entities that are users associated with a client, wherein the first dataset comprises any of mobile entity identifiers, locations, or hashed email addresses of the users;
obtaining, at the server, a second dataset of a second set of entities from the entity devices in a geographical area, wherein the second dataset comprises the location data streams comprising any of device attributes, connection attributes, and user agent strings
obtaining, at the server, ground truth labels based on the high confident entities from the first dataset;
training a deep similarity model based on the ground truth labels and at least one custom feature specific to the client to obtain a trained deep similarity model, wherein the trained deep similarity model determines attributes associated with the high confident entities from the first dataset; and
determining, at the server, using the trained deep similarity model and a classification method on the second dataset, the cluster of the device identifiers associated with the entity devices of the entities having the attributes that are similar to the high confident entities from the first dataset, from the second dataset.
18 . The non-transitory computer readable storage medium storing a sequence of instructions of claim 17 , the sequence of instructions further comprising
determining, using the trained deep similarity model and one-class classification method on the second dataset, the cluster of the device identifiers associated with the entity devices of the entities having the attributes that are similar to the high confident entities from the first dataset, from the second dataset, wherein the device identifiers are obtained when a plurality of first behavioral attributes of a matched set of entities are similar to a plurality of second behavioral attributes of the second set of entities while comparing each other.
19 . The non-transitory computer readable storage medium storing a sequence of instructions of claim 17 , the sequence of instructions further comprising
determining, using the trained deep similarity model and a binary-class classification method, the cluster of the device identifiers associated with the entity devices of the entities having a combination of the attributes that are similar to the high confident entities from the first dataset and the attributes that are contrary to the high confident entities from the first dataset, from the second dataset, wherein the entities with the attributes that are contrary to the high confident entities from the first dataset comprise a first entity from a matched set of entities and a second entity from the second set of entities, wherein at least one attribute of the first entity is mutually exclusive from at least one attribute of the second entity.
20 . The non-transitory computer-readable storage medium storing a sequence of instructions of claim 17 , the sequence of instructions further comprising
determining, using the trained deep similarity model and a multi-class classification method, the cluster of the device identifiers having multiple overlapping attributes of behavior to the high confident entities from the first dataset, from the second dataset, wherein the device identifiers having the multiple overlapping attributes of behavior to the high confident entities from the first dataset, are obtained when a plurality of first behavioral attributes of a matched set of entities overlap in comparison with a plurality of second behavioral attributes of the second set of entities.