DESTINATION ARTIFICIAL INTELLIGENCE

1 . A method, comprising:

identifying contextual user information of a user accessing a first page of a website comprising a plurality of content items;

inputting the contextual user information and content information for the plurality of content items of the website into a model trained to generate recommendations of what content items to show users as part of user journeys within websites;

generating, by the model using the contextual user information and the content information as input, a sequence of content items to recommend to the user, wherein the sequence of content items are defined as a journey for the user through the website that will result in an increased probability of satisfying an objective for the user to make an informed decision;

populating an interface element of the website with one or more content items from the sequence of content items; and

in response to the user interfacing with a content item within the interface element, transitioning the website to displaying the content item.

2 . The method of claim 1 , comprising:

detecting the user accessing a second page of the website;

generating current contextual information related to the user accessing the website;

inputting the current contextual information into the model to generate an updated sequence of content items; and

populating the interface element with one or more content items from the sequence of content items.

3 . The method of claim 1 , comprising:

utilizing the model to generate a set of qualifying questions for the user, wherein the model is trained to minimize a number of qualifying questions to include within the set of qualifying questions;

populating a user interface with set of qualifying questions; and

utilizing user specified answers to one or more qualifying questions within the set of qualifying answers as the contextual user information.

4 . The method of claim 1 , comprising:

evaluating an output, including the sequence of content items, to determine that a recommended content item to show to the user is not included within the website; and

generating a notification with instructions to create the recommended content item for inclusion within the website.

5 . The method of claim 1 , comprising:

evaluating an output, including the sequence of content items, to determine that a recommended content item to show to the user is not included within the website;

generating the recommended content item; and

updating the website to include the recommended content item.

6 . The method of claim 1 , comprising:

hosting a website service for the website and the model within a computing environment for performing localized inferences for generating suggestions of content items to provide users, qualifying questions to ask the users, and guidance for navigating the users through journeys across content items of the website.

7 . The method of claim 1 , comprising:

training the model with user click stream data corresponding to users interacting with content items of the website, wherein the model is trained with information about the users and resulting actions performed by the users in relation to objectives for users navigating through the website.

8 . The method of claim 1 , comprising:

outputting, by the model, the sequence of content items to include content items that similar users viewed along journeys of navigating the website that resulted in the users performing actions related to the objective.

9 . A computing device comprising:

a memory comprising machine executable code; and

a processor coupled to the memory, the processor configured to execute the machine executable code to cause the processor to perform operation comprising:

identifying contextual user information of a user accessing a first page of a website comprising a plurality of content items;

inputting the contextual user information and content information for the plurality of content items of the website into a model trained to generate recommendations of what content items to show users as part of user journeys within websites;

generating, by the model using the contextual user information and the content information as input, a sequence of content items to recommend to the user, wherein the sequence of content items are defined as a journey for the user through the website that will result in an increased probability of satisfying an objective for the user to make an informed decision;

populating an interface element of the website with one or more content items from the sequence of content items; and

in response to the user interfacing with a content item within the interface element, transitioning the website to displaying the content item.

10 . The computing device of claim 9 , wherein the operations comprise:

identifying, using the model, a content item that was viewed by a past user having one or more similar attributes as the user, wherein the past user performed an action related to the objective for the user.

11 . The computing device of claim 9 , wherein the operations comprise:

clustering, using the model, content item of the website and users having similar attributes that performed similar actions after navigating across the website to create clusters; and

utilizing the clusters to identify the sequence of content items to recommend to the user.

12 . The computing device of claim 9 , wherein the sequence of content items are similar to sequences of content items navigated amongst by similar users as the user given the objective for the user being similar to actions performed by the similar users, and wherein the contextual user information includes previously viewed content and information by the user before visiting the first page.

13 . The computing device of claim 9 , wherein the operations comprise:

storing the contextual user information within local storage associated with a browser accessing the website as stored contextual user information protected from being accessed by services or analytics other than a recommendation engine hosting the model.

14 . The computing device of claim 9 , wherein the interface element is a widget, and wherein the operations comprise:

displaying the sequence of content items through the widget, wherein a title, a text description, and an image are displayed for each content item.

15 . A non-transitory machine-readable storage medium comprising instructions that when executed by a machine, causes the machine to perform operations comprising:

identifying contextual user information of a user accessing a first page of a website comprising a plurality of content items;

inputting the contextual user information and content information for the plurality of content items of the website into a model trained to generate recommendations of what content items to show users as part of user journeys within websites;

generating, by the model using the contextual user information and the content information as input, a sequence of content items to recommend to the user, wherein the sequence of content items are defined as a journey for the user through the website that will result in an increased probability of satisfying an objective for the user to make an informed decision;

populating an interface element of the website with one or more content items from the sequence of content items; and

in response to the user interfacing with a content item within the interface element, transitioning the website to displaying the content item.

16 . The non-transitory machine-readable storage medium of claim 15 , wherein the operations comprise:

receiving feedback from the user for the content item; and

training the model based upon the feedback, wherein a weight used to select or rank the content item is reduced based upon the feedback being negative feedback.

17 . The non-transitory machine-readable storage medium of claim 15 , wherein the operations comprise:

receiving feedback from the user for the content item; and

modifying a weight associated with the content item to suppress selection of the content item by the model for similar users as the user.

18 . The non-transitory machine-readable storage medium of claim 15 , wherein the operations comprise:

receiving feedback from the user for the content item; and

modifying a weight associated with a cluster including the content item to suppress select of content items from the cluster by the model for similar users as the user.

19 . The non-transitory machine-readable storage medium of claim 15 , wherein the operations comprise:

displaying the sequence of content items through the interface element as a series of representations linking to the content items.

20 . The non-transitory machine-readable storage medium of claim 15 , wherein the operations comprise:

requesting feedback from the user as to whether the user performed an action based upon content items navigated by the user leading up to the action being performed.