Deployment and customization of applications at the widget level
This disclosure relates to customizing deployment of an application to a user interface of a client device. An exemplary method generally includes training a model based on historical context information of a plurality of users by identifying correlations between the historical context information and a plurality of widgets and storing the correlations in the model. The method further includes receiving context information from the client device. The method further includes determining a user intent based on the context information using the model. The method further includes selecting one or more widgets to include in a custom user interface definition based, at least in part, on the user intent. The method further includes transmitting, to the user interface of the client device, the custom user interface definition.
1. A method for customizing deployment of an application to a user interface of a client device, comprising:
receiving context information from the client device;
determining device capabilities of the client device based on the context information;
determining one or more relevant widgets of a plurality of widgets based on the context information;
determining a set of widgets based on the device capabilities of the client device and the one or more relevant widgets;
using a model to determine a relevance score for each widget of the set of widgets based on the context information, wherein the model was trained based on historical context information of a plurality of users;
including the set of widgets in a custom user interface definition based on the relevance score for each widget of the set of widgets, wherein:
a first widget of the set of widgets corresponds to an image capture-based method of ingesting particular data;
a second widget of the set of widgets corresponds to a manual entry-based method of ingesting the particular data;
an order of the first widget and the second widget within the custom user interface definition indicates that the image capture-based method of ingesting the particular data is preferred over the manual entry-based method of ingesting the particular data; and
an order of the set of widgets is determined based, at least in part, on priorities associated with the set of widgets; and
transmitting, to the user interface of the client device, the custom user interface definition.
2. The method of claim 1 , wherein the context information comprises at least one of: clickstream data of the client device; one or more user preferences; or user profile information.
3. The method of claim 1 , further comprising:
comparing the context information to historical context information associated in the model with each of the plurality of widgets in order to determine a plurality of similarity metrics; and
selecting the set of widgets based, at least in part, on the similarity metrics.
4. The method of claim 3 , wherein the order of the set of widgets within the custom user interface definition is determined based further on the relevance score for each widget of the set of widgets.
5. The method of claim 3 , wherein the set of widgets is selected, at least in part, by applying one or more predetermined rules based on the context information.
6. The method of claim 3 , wherein determining the plurality of similarity metrics comprises:
storing the context information in a vector; and
using cosine similarity to determine the plurality of similarity metrics between the vector and each of a plurality of historical vectors representing the historical context information that is associated in the model with each of the plurality of widgets.
7. The method of claim 1 , further comprising displaying, via the user interface of the client device, the set of widgets to enable a user to complete a step in a workflow on the client device.
8. A system, comprising:
a processor; and
a memory having instructions stored thereon which, when executed by the processor, performs an operation for customizing deployment of an application to a user interface of a client device, the operation comprising:
receiving context information from the client device;
determining device capabilities of the client device based on the context information;
determining one or more relevant widgets of a plurality of widgets based on the context information;
determining a set of widgets based on the device capabilities of the client device and the one or more relevant widgets;
using a model to determine a relevance score for each widget of the set of widgets based on the context information, wherein the model was trained based on historical context information of a plurality of users; and
including the set of widgets in a custom user interface definition based on the relevance score for each widget of the set of widgets, wherein:
a first widget of the set of widgets corresponds to an image capture-based method of ingesting particular data;
a second widget of the set of widgets corresponds to a manual entry-based method of ingesting the particular data;
an order of the first widget and the second widget within the custom user interface definition indicates that the image capture-based method of ingesting the particular data is preferred over the manual entry-based method of ingesting the particular data; and
an order of the set of widgets is determined based, at least in part, on priorities associated with the set of widgets; and
transmitting, to the user interface of the client device, the custom user interface definition.
9. The system of claim 8 , wherein the context information comprises at least one of: clickstream data of the client device; one or more user preferences; or user profile information.
10. The system of claim 8 , further comprising:
comparing the context information to historical context information associated in the model with each of the plurality of widgets in order to determine a plurality of similarity metrics; and
selecting the set of widgets based, at least in part, on the similarity metrics.
11. The system of claim 10 , wherein the order of the set of widgets within the custom user interface definition is determined based further on the relevance score for each widget of the set of widgets.
12. The system of claim 10 , wherein the set of widgets is selected, at least in part, by applying one or more predetermined rules based on the context information.
13. The system of claim 10 , wherein determining the plurality of similarity metrics comprises:
storing the context information in a vector; and
using cosine similarity to determine the plurality of similarity metrics between the vector and each of a plurality of historical vectors representing the historical context information that is associated in the model with each of the plurality of widgets.
14. The system of claim 8 , further comprising displaying, via the user interface of the client device, the set of widgets to enable a user to complete a step in a workflow on the client device.
15. A method for customizing deployment of an application to a user interface of a client device, comprising:
receiving context information from the client device;
determining device capabilities of the client device based on the context information;
determining one or more relevant widgets of a plurality of widgets based on the context information;
determining a set of widgets based on the device capabilities of the client device and the one or more relevant widgets;
using a model to determine a relevance score for each widget of the set of widgets based on the context information, wherein the model was trained based on historical context information of a plurality of users; and
including the set of widgets in a custom user interface definition based on the relevance score for each widget of the set of widgets, wherein:
a first widget of the set of widgets corresponds to an image capture-based method of ingesting particular data;
a second widget of the set of widgets corresponds to a manual entry-based method of ingesting the particular data;
an order of the first widget and the second widget within the custom user interface definition indicates that the image capture-based method of ingesting the particular data is preferred over the manual entry-based method of ingesting the particular data; and
an order of the set of widgets is determined based, at least in part, on priorities associated with the set of widgets; and
transmitting, to the user interface of the client device, the custom user interface definition.
16. The method of claim 15 , wherein the context information comprises at least one of: clickstream data of the client device; one or more user preferences; or user profile information.
17. The method of claim 15 , further comprising:
comparing the context information to historical context information associated in the model with each of the plurality of widgets in order to determine a plurality of similarity metrics; and
selecting the set of widgets based, at least in part, on the similarity metrics.
18. The method of claim 17 , wherein the order of the set of widgets within the custom user interface definition is determined based further on the relevance score for each widget of the set of widgets.
19. The method of claim 17 , wherein the set of widgets is selected, at least in part, by applying one or more predetermined rules based on the context information.
20. The method of claim 17 , wherein determining the plurality of similarity metrics comprises:
storing the context information in a vector; and
using cosine similarity to determine the plurality of similarity metrics between the vector and each of a plurality of historical vectors representing the historical context information that is associated in the model with each of the plurality of widgets.