RECOMMENDATION PLATFORM

• US 20160104067A1
• Filed: 05/11/2015
• Published: 04/14/2016
• Est. Priority Date: 10/08/2014
• Status: Abandoned Application

• Current Assignee: Salesforce.com, Inc.
• Original Assignee: Salesforce.com, Inc.
• Inventors: Xu, Zhenhua, Palmert, Joel, Mencke, Rasmus, White, Scott Douglas, Louie, Arthur Albert, Seradjfar, Kamyar, Peng, Weiping, Magnusson, Johan Philip, Hird, Zandra, Chen, Ziwei, Agarwal, Kapil, Doti, Adam McCormick, Byrne, Paul Gene, Mao, Runying
• Application Number:
  US14/709,218
• Publication Number:
  US 20160104067A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: H04L 67/306 User profiles
• CPC Class Codes: H04L 67/535 Tracking the activity of the user network monitoring arrangements H04L43/00; recording of computer activity G06F11/34

Abstract

Disclosed are some examples of systems, apparatus, methods and storage media for providing customized recommendations to users. Some implementations more particularly relate to a recommendation platform that enables authorized third parties to create, customize and add new recommendations that are then available to be served to target users or audiences of users. Some implementations further relate to a recommendation platform that enables authorized users to define audiences, scheduling settings, scheduling policies, and rules to customize or influence the provision of associated recommendations to the users. The recommendation platform includes a recommendation engine that serves the recommendations to users based on such defined audiences, scheduling settings, policies or other rules.

Claims

1. A database system configurable to:

maintain a database configurable to store;

organization data for a plurality of organizations that are tenants of the database system;

a plurality of recommendation definitions, each recommendation definition defining a respective recommendation; and

a plurality of audience definitions, each audience definition defining an audience of users;

host a recommendation engine configurable to serve recommendations based on the recommendation definitions and the audience definitions; and

host at least one application programming interface (API) defining interactions between the recommendation engine and the database, the API configurable to enable an authorized third party user to;

create a recommendation definition or edit an existing recommendation definition in the database;

create an audience definition or edit an existing audience definition in the database; and

associate a recommendation definition with an audience definition.

2. The database system of claim 1, wherein:

the API is configurable to;

determine that a recommendation is to be served to a user, and

identify one or more audience definitions that include the user; and

the recommendation engine is configurable to;

identify one or more recommendation definitions associated with the identified audience definitions,

select a recommendation definition from the identified recommendation definitions; and

the API is further configurable to;

serve the respective recommendation to the user based on the selected recommendation definition.

3. The database system of claim 2, wherein recommendation engine is configurable to:

prioritize the recommendation definitions in the identified recommendation definitions; and

select the recommendation definition having the highest priority.

4. The database system of claim 3, wherein the recommendation engine is configurable to prioritize the recommendation definitions based on one or more associated audience definitions.

5. The database system of claim 3, wherein the recommendation engine is configurable to prioritize the recommendation definitions based on one or more associated scheduling policies.

6. The database system of claim 3, wherein the recommendation engine is configurable to prioritize the recommendation definitions for the user based a determined level of engagement of the user with a social network.

7. The database system of claim 3, wherein the recommendation engine is configurable to prioritize the recommendation definitions based on historical data collected on previous servings of recommendations associated with one or more of the identified recommendation definitions.

8. The database system of claim 1, wherein the authorized third party user is an administrator of one of the organizations.

9. The database system of claim 1, wherein the API is configurable to enable the authorized third party user to associate a recommendation definition with multiple audience definitions.

10. The database system of claim 1, wherein the API is configurable to enable the authorized third party user to associate an audience definition with multiple recommendation definitions.

11. The database system of claim 1, wherein each recommendation definition includes one or more of:

a title of the recommendation, a type of the recommendation, a description of the recommendation, content of the recommendation, an identifier of a location where content of the recommendation is accessible, and an action associated with the recommendation.

12. The database system of claim 1, wherein each audience definition includes one or more of:

one or more user types, one or more user profile types, one or more sets of one or more permissions, one or more group types, one or more community types.

13. The database system of claim 1, wherein:

the database further stores a plurality of schedule definitions, each schedule definition defining one or more serving parameters for serving an associated recommendation; and

the API is configurable to enable the authorized user to create a schedule definition or edit an existing schedule definition, and associate a schedule definition with one or more recommendation definitions.

14. The database system of claim 13, wherein the API is configurable to enable the authorized user to associate a schedule definition with one or more audience definitions, the schedule definition further defining which serving parameters are associated with each of the audience definitions.

15. The database system of claim 13, wherein the one or more serving parameters include one or more of:

a start date indicating a time after which the associated recommendation is permitted to be served by the recommendation engine;

an end date indicating a time after which the associated recommendation is no longer permitted to be served by the recommendation engine;

a serving frequency indicating a frequency at which the associated recommendation is to be served;

a maximum frequency indicating a maximum frequency at which the associated recommendation is to be served;

a maximum number indicating a maximum number of times the associated recommendation is to be served;

a time of day at or during which the associated recommendation is to be served; and

a day of the week during which the associated recommendation is to be served.

16. The database system of claim 1, wherein:

the database further stores a plurality of rule definitions, each rule definition defining one or more rules for serving an associated recommendation; and

the API is configurable to enable the authorized user to create a rule definition or edit an existing rule definition, and associate a rule definition with one or more recommendation definitions or one or more audience definitions.

17. The database system of claim 16, wherein each rule definition includes one or more of:

a combination of one or more actions that trigger the recommendation engine to serve an associated recommendation;

a combination of one or more inactions that trigger the recommendation engine to serve an associated recommendation; and

a combination of one or more actions and one or more inactions that trigger the recommendation engine to serve an associated recommendation.

18. The database system of claim 1, wherein the recommendations corresponding to the recommendation definitions can include recommendations for one or more of:

another user to follow, a group to subscribe to or a community to join.

19. The database system of claim 1, wherein the recommendations corresponding to the recommendation definitions can include recommendations for an event to attend.

20. The database system of claim 1, wherein the recommendations corresponding to the recommendation definitions can include recommendations for one or more of:

a software product to buy or try, a cloud-service-based product to buy or try or a tangible product to buy or try.

21. The database system of claim 1, wherein the database system is configurable to generate one or more reports or performance metrics based on the serving of the recommendations.

22. A database system configurable to:

maintain a database storing;

organization data associated with at least one organization;

a plurality of recommendation definitions, each recommendation definition defining a respective recommendation; and

a plurality of audience definitions, each audience definition defining an audience of users; and

host a recommendation engine and at least one application programming interface (API) configurable to interact with the recommendation engine and the database to;

detect an action;

identify a user associated with the action;

identify one or more audience definitions that include the user;

identify one or more recommendation definitions associated with the identified audience definitions;

select a recommendation definition from the identified recommendation definitions; and

serve the respective recommendation to the user based on the selected recommendation definition.

23. The database system of claim 22, wherein the user to whom the recommendation is served is a user within the organization.

24. The database system of claim 22, wherein the user to whom the recommendation is served is a user external to the organization.

25. The database system of claim 22, wherein the recommendation is served as a feed item in a social networking feed.

26. The database system of claim 22, wherein recommendation engine is configurable to:

prioritize the recommendation definitions in the identified recommendation definitions; and

select the recommendation definition having the highest priority.

27. The database system of claim 26, wherein the recommendation engine is configurable to prioritize the recommendation definitions based on one or more associated audience definitions.

28. The database system of claim 26, wherein the recommendation engine is configurable to prioritize the recommendation definitions based on one or more associated scheduling policies.

29. The database system of claim 26, wherein the recommendation engine is configurable to prioritize the recommendation definitions for the user based a determined level of engagement of the user with a social network.

30. The database system of claim 26, wherein the recommendation engine is configurable to prioritize the recommendation definitions based on historical data collected on previous servings of recommendations associated with one or more of the identified recommendation definitions.