Intelligent contextual help chat in a multi-tenant database system

1. A method, comprising:

identifying a user of a client device based, at least in part, on a message received from the client device;

obtaining a set of privacy settings associated with the user, the set of privacy settings including one or more indicators, at least one indicator indicating whether access to a value of a corresponding one of a plurality of contextual features is permissible, the plurality of contextual features including one or more tenant features;

identifying, in a database, a user profile associated with the user;

identifying a tenant associated with the user, the tenant being one of a plurality of tenants of a multi-tenant database;

ascertaining a context of the user according to the set of privacy settings based, at least in part, on the user profile and tenant information associated with the tenant, the tenant information indicating one or more resources available to the tenant, the context including values corresponding to at least a portion of the plurality of contextual features;

generating a recommendation based, at least in part, on the context; and

providing the recommendation.

2. The method as recited in claim 1 , the context including at least one value corresponding to at least one tenant feature of the tenant features.

3. The method as recited in claim 1 , the tenant features comprising one or more of: a number of feature licenses purchased, a number of feature licenses used, a number of feature licenses available, a number of user licenses purchased, a number of user licenses used, a number of user licenses available, a maximum amount of storage available, an amount of storage used, or an amount of storage remaining.

4. The method as recited in claim 1 , the tenant features comprising one or more of: a product, a product edition, a product status, a product feature set, or an amount of time remaining in a trial period for the product.

5. The method as recited in claim 1 , at least one of the indicators indicating whether access to a value of a corresponding one of the user features is permissible.

6. The method as recited in claim 1 , the user features comprising one or more of: a user license type, a user profile type, a user role, or recent online activities.

7. The method as recited in claim 1 , the method further comprising:

generating the recommendation by applying the context using a machine learning model including a plurality of weights, each of the weights corresponding to a different one of the plurality of contextual features.

8. A system comprising:

a database system implemented using a server system, the database system configurable to cause:

identifying a user of a client device based, at least in part, on a message received from the client device;

obtaining a set of privacy settings associated with the user, the set of privacy settings including one or more indicators, at least one indicator indicating whether access to a value of a corresponding one of a plurality of contextual features is permissible, the plurality of contextual features including one or more tenant features;

identifying, in a database, a user profile associated with the user;

identifying a tenant associated with the user, the tenant being one of a plurality of tenants of a multi-tenant database;

ascertaining a context of the user according to the set of privacy settings based, at least in part, on the user profile and tenant information associated with the tenant, the tenant information indicating one or more resources available to the tenant, the context including values corresponding to at least a portion of the plurality of contextual features;

generating a recommendation based, at least in part, on the context; and

providing the recommendation.

9. The system as recited in claim 8 , the context including at least one value corresponding to at least one tenant feature of the tenant features.

10. The system as recited in claim 8 , the tenant features comprising one or more of: a number of feature licenses purchased, a number of feature licenses used, a number of feature licenses available, a number of user licenses purchased, a number of user licenses used, a number of user licenses available, a maximum amount of storage available, an amount of storage used, or an amount of storage remaining.

11. The system as recited in claim 8 , the tenant features comprising one or more of: a product, a product edition, a product status, a product feature set, or an amount of time remaining in a trial period for the product.

12. The system as recited in claim 8 , at least one of the indicators indicating whether access to a value of a corresponding one of the user features is permissible.

13. The system as recited in claim 8 , the user features comprising one or more of: a user license type, a user profile type, a user role, or recent online activities.

14. The system as recited in claim 8 , the database system further configurable to cause:

generating the recommendation by applying the context using a machine learning model including a plurality of weights, each of the weights corresponding to a different one of the plurality of contextual features.

15. A computer program product comprising computer-readable program code capable of being executed by one or more processors when retrieved from a non-transitory computer-readable medium, the program code comprising computer-readable instructions configurable to cause:

identifying a user of a client device based, at least in part, on a message received from the client device;

obtaining a set of privacy settings associated with the user, the set of privacy settings including one or more indicators, at least one indicator indicating whether access to a value of a corresponding one of a plurality of contextual features is permissible, the plurality of contextual features including one or more tenant features;

identifying, in a database, a user profile associated with the user;

identifying a tenant associated with the user, the tenant being one of a plurality of tenants of a multi-tenant database;

ascertaining a context of the user according to the set of privacy settings based, at least in part, on the user profile and tenant information associated with the tenant, the tenant information indicating one or more resources available to the tenant, the context including values corresponding to at least a portion of the plurality of contextual features;

generating a recommendation based, at least in part, on the context; and

providing the recommendation.

16. The computer program product as recited in claim 15 , the context including at least one value corresponding to at least one tenant feature of the tenant features.

17. The computer program product as recited in claim 8 , the tenant features comprising one or more of: a number of feature licenses purchased, a number of feature licenses used, a number of feature licenses available, a number of user licenses purchased, a number of user licenses used, a number of user licenses available, a maximum amount of storage available, an amount of storage used, or an amount of storage remaining.

18. The computer program product as recited in claim 8 , the tenant features comprising one or more of: a product, a product edition, a product status, a product feature set, or an amount of time remaining in a trial period for the product.

19. The computer program product as recited in claim 8 , at least one of the indicators indicating whether access to a value of a corresponding one of the user features is permissible.

20. The computer program product as recited in claim 8 , the user features comprising one or more of: a user license type, a user profile type, a user role, or recent online activities.