Machine learning model and encoder to predict online user journeys
The subject technology identifies a series of journey event types in an online user journey, the event types including an impression event, an email event, a click event, and a website visit, and assigns an encoder to each event type. Using an assigned encoder, the technology encodes each event type to generate an encoded vector for each event type. The encoded vector is representative of at least a portion of the online user journey relating to that event type. The technology generates an encoded vector for each event type to create a set of encoded vectors, the set of encoded vectors including one or more of an impression event encoded vector, an email event encoded vector, a click event encoded vector, and a website visit encoded vector. The technology aggregates the set of encoded vectors to generate an output of the online user journey encoder, the output including a composite encoded user journey vector for modeling, transmits the output of the online user journey encoder to a user journey training model for training of the model and, using a trained model, generates an occurrence probability for at least one further event in the online user journey.
1 . A system for predicting online user activity comprising:
one or more processors; and
a memory storing instructions which, when executed by at least one processor in the one or more processors, cause the at least one processor to:
identify multiple journey event types in a sample of multiple online user journeys;
assign an encoder to each event type;
use an assigned encoder to encode each instance of a particular event type in the sample to generate an encoded vector for the particular event type, the encoded vector being representative of at least a portion of one or more of the multiple online user journeys related to the particular event type;
generate an encoded vector for each event type to create a set of encoded vectors for modeling;
transmit the set of encoded vectors to a user journey training model for training of a user journey training model;
use the trained user journey training model to generate an occurrence probability for multiple further events in an online user journey;
append a theoretical further event to the online user journey to generate a test online user journey, the theoretical further event selected from the multiple further events based on the occurrence probability;
use an assigned encoder to encode each instance of a particular event type in the test online user journey to create a set of test encoded vectors; and
evaluate the set of test encoded vectors using the trained user journey training model to generate an occurrence probability for at least one further event in the test online user journey.
2 . The system of claim 1 , wherein the user journey training model corresponds to a long short-term memory (LSTM) neural network.
3 . The system of claim 1 , wherein the at least one further event comprises a conversion event.
4 . The system of claim 3 , wherein the at least one processor is further configured to determine how the occurrence probability of the conversion event is affected by differing content items associated with one or more of the multiple journey event types.
5 . The system of claim 3 , wherein the at least one processor is further configured to optimize an occurrence probability of the conversion event using differing content items associated with one or more of the multiple journey event types.
6 . The system of claim 1 , wherein the at least one processor is further configured to sequence the encoded vectors in the set of test encoded vectors in at least one of a time-based order and event type order; and
aggregate the sequenced encoded vectors to generate an output of an online user journey encoder, the output including a composite encoded test user journey vector.
7 . The system of claim 6 , wherein the at least one processor is further configured to augment the composite encoded test user journey vector based on one or more of a media type, a timing, a media channel, an image, and a text or email content.
8 . The system of claim 1 , wherein the at least one processor is further configured to map each instance of one or more of the multiple event types to a unique user identifier.
9 . A method of predicting online user activity, the method comprising:
identifying multiple journey event types in a sample of multiple online user journeys;
assigning an encoder to each event type;
using an assigned encoder, encoding each instance of a particular event type in the sample to generate an encoded vector for the particular event type, the encoded vector being representative of at least a portion of one or more of the multiple online user journeys related to the particular event type;
generating an encoded vector for each event type to create a set of encoded vectors for modeling;
transmitting the set of encoded vectors to a user journey training model for training of a user journey training model;
using the trained user journey training model to generate an occurrence probability for multiple further events in an online user journey;
appending a theoretical further event to the online user journey to generate a test online user journey, the theoretical further event selected from the multiple further events based on the occurrence probability;
using an assigned encoder, encoding each instance of a particular event type in the test online user journey to create a set of test encoded vectors; and
evaluating the set of test encoded vectors using the trained user journey training model to generate an occurrence probability for at least one further event in the test online user journey.
10 . The method of claim 9 , wherein the user journey training model corresponds to a long short-term memory (LSTM) neural network.
11 . The method of claim 9 , wherein the at least one further event comprises a conversion event.
12 . The method of claim 11 , further comprising determining how the occurrence probability of the conversion event is affected by differing content items associated with one or more of the multiple journey event types.
13 . The method of claim 11 , further comprising optimizing an occurrence probability of the conversion event using differing content items associated with one or more of the multiple journey event types.
14 . The method of claim 9 , further comprising sequencing the encoded vectors in the set of test encoded vectors in at least one of a time-based order or event type order; and
aggregating the sequenced encoded vectors to generate an output of an online user journey encoder, the output including a composite encoded test user journey vector.
15 . The method of claim 14 , further comprising augmenting the composite encoded test user journey vector based on one or more of a media type, a timing, a media channel, an image, and a text or email content.
16 . The method of claim 9 , further comprising mapping each instance of one or more of the multiple event types to a unique user identifier.
17 . A non-transitory machine-readable medium comprising instructions which, when read by a machine, cause the machine to perform operations in a method of predicting online user activity, the operations comprising:
identifying multiple journey event types in a sample of multiple online user journeys;
assigning an encoder to each event type;
using an assigned encoder, encoding each instance of a particular event type in the sample to generate an encoded vector for the particular event type, the encoded vector being representative of at least a portion of one or more of the multiple online user journeys related to the particular event type;
generating an encoded vector for each event type to create a set of encoded vectors for modeling;
transmitting the set of encoded vectors to a user journey training model for training of a user journey training model;
using the trained user journey training model to generate an occurrence probability for multiple further events in an online user journey;
appending a theoretical further event to the online user journey to generate a test online user journey, the theoretical further event selected from the multiple further events based on the occurrence probability;
using an assigned encoder, encoding each instance of a particular event type in the test online user journey to create a set of test encoded vectors; and
evaluating the set of test encoded vectors using the trained user journey training model to generate an occurrence probability for at least one further event in the test online user journey.
18 . The medium of claim 17 , wherein the at least one further event comprises a conversion event.
19 . The medium of claim 18 , wherein the operations further comprise determining how the occurrence probability of the conversion event is affected by differing content items associated with one or more of the multiple journey event types.
20 . The medium of claim 18 , wherein the operations further comprise optimizing an occurrence probability of the conversion event using differing content items associated with one or more of the multiple journey event types.