Ranking information items by relevance

• US 9,807,185 B2
• Filed: 09/02/2015
• Issued: 10/31/2017
• Est. Priority Date: 06/03/2011
• Status: Active Grant

• Current Assignee: Meta Platforms, Inc. (f/k/a Facebook, Inc.)
• Original Assignee: Meta Platforms, Inc. (f/k/a Facebook, Inc.)
• Inventors: Tseng, Erick
• Primary Examiner(s): Nooristany, Sulaiman
• Application Number:
  US14/843,867
• Publication Number:
  US 20150381750A1
• Time in Patent Office: 790 Days
• Field of Search: None
• CPC Class Codes: G06Q 30/0269 based on user profile or attribute
• CPC Class Codes: G06Q 50/01 Social networking
• CPC Class Codes: H04L 67/306 User profiles
• CPC Class Codes: H04L 67/53 using third party service providers
• CPC Class Codes: H04L 67/535 Tracking the activity of the user network monitoring arrangements H04L43/00; recording of computer activity G06F11/34
• CPC Class Codes: H04L 67/55 Push-based network services

Abstract

In one embodiment, a method includes collecting, by one or more computing devices, multiple information items of multiple types relevant to a user, where the information items are collected from a social-networking system, and the user is a member of the social-networking system. The method further includes determining a relevance value for each of the information items and classifying each information item as being deterministic or non-deterministic based at least in part on the relevance value. The method also includes sending one or more push notifications to one or more client devices associated with the user, the push notifications including one or more of the information items classified as non-deterministic sent in an order of their respective relevance values. The method also includes storing the information items classified as deterministic for later retrieval by the user.

Claims

1. A method comprising:

collecting, by one or more computing devices, a plurality of information items of a plurality of types relevant to a user, wherein;

the information items are collected from a social-networking system; and

the user is a member of the social-networking system;

determining, by one or more computing devices, a relevance value for each of the information items, wherein the relevance value comprises a product of a rank boosting factor and relevance factors, and the rank boosting factor is based on whether an importance level of the information item decreases as time passes;

wherein the relevance factors comprise a time relevance, a location relevance, and a social graph relevance,

wherein the time relevance is based on how close a time associated with the information item is to a current time, the location relevance is based on how close a physical location associated with the information item is to a current physical location of the user, and the social graph relevance is based on how close a node representing the information item is to a node representing the user in a graph associated with the social-networking system, the graph representing information contained in the social-networking system;

sending, by one or more computing devices, one or more push notifications to one or more client devices associated with the user, the push notifications comprising one or more of the information items sent in an order of their respective relevance values.

2. The method of claim 1, wherein the information items comprise a first information item and a second information item,

the importance level of the first information item decreases as time passes,

the importance level of the second information item increases or remains constant as time passes, and

a first rank boosting factor that corresponds to the first information item is greater than a second rank boosting factor that corresponds to the second information item.

3. The method of claim 1, wherein the rank boosting factor increases the relevance value of the information item when an importance level of the information item decreases as time passes.

4. The method of claim 1, wherein:

each of the information items having a relevance value that remains substantially constant over time is classified as deterministic; and

each of the information items having a relevance value that decreases with time is classified as non-deterministic.

5. The method of claim 1, further comprising boosting a relevance value of an information item associated with a location that is close to a current location of the user.

6. The method of claim 1, further comprising boosting a relevance value of an information item associated with a time that is close to a current time.

7. The method of claim 4, further comprising excluding from the push notifications one or more information items classified as non-deterministic and being of a particular type specified by the user.

8. The method of claim 1, wherein the information items are further collected from at least one third-party system.

19. The method of claim 5, wherein the rank boosting factor increases the ranking score when the information item is associated with a location that is the same as or near a current location of the user as indicated by a current location of the one or more computing devices.

20. The method of claim 6, wherein the rank boosting factor increases the ranking score when the information item comprises an event having an associated time close to a current time.

9. One or more computer-readable non-transitory storage media embodying software that is operable when executed to:

collect a plurality of information items of a plurality of types relevant to a user, wherein;

the information items are collected from a social-networking system; and

the user is a member of the social-networking system;

determine a relevance value for each of the information items, wherein the relevance value comprises a product of a rank boosting factor and relevance factors, and the rank boosting factor is based on whether an importance level of the information item decreases as time passes;

wherein the relevance factors comprise a time relevance, a location relevance, and a social graph relevance,

wherein the time relevance is based on how close a time associated with the information item is to a current time, the location relevance is based on how close a physical location associated with the information item is to a current physical location of the user, and the social graph relevance is based on how close a node representing the information item is to a node representing the user in a graph associated with the social-networking system, the graph representing information contained in the social-networking system;

send one or more push notifications to one or more client devices associated with the user, the push notifications comprising one or more of the information items.

10. The media of claim 9, wherein the information items comprise a first information item and a second information item,

the importance level of the first information item decreases as time passes,

the importance level of the second information item increases or remains constant as time passes, and

a first rank boosting factor that corresponds to the first information item is greater than a second rank boosting factor that corresponds to the second information item.

11. The media of claim 9, wherein the rank boosting factor increases the relevance value of the information item when an importance level of the information item decreases as time passes.

12. The media of claim 9, wherein:

each of the information items having a relevance value that remains substantially constant over time is classified as deterministic; and

each of the information items having a relevance value that decreases with time is classified as non-deterministic.

13. The media of claim 9, wherein the software is further operable when executed to boost a relevance value of an information item associated with a location that is close to a current location of the user.

14. The media of claim 9, wherein the software is further operable when executed to boost a relevance value of an information item associated with a time that is close to a current time.

15. The media of claim 12, wherein the software is further operable when executed to exclude from the push notifications one or more information items classified as non-deterministic and being of a particular type specified by the user.

16. The media of claim 9, wherein the information items are further collected from at least one third-party system.

17. A system comprising:

one or more processors; and

a memory coupled to the processors comprising instructions executable by the processors, the processors being operable when executing the instructions to;

collect a plurality of information items of a plurality of types relevant to a user, wherein;

the information items are collected from a social-networking system; and

the user is a member of the social-networking system;

determine a relevance value for each of the information items, wherein the relevance value comprises a product of a rank boosting factor and relevance factors, and the rank boosting factor is based on whether an importance level of the information item decreases as time passes;

wherein the relevance factors comprise a time relevance, a location relevance, and a social graph relevance,

wherein the time relevance is based on how close a time associated with the information item is to a current time, the location relevance is based on how close a physical location associated with the information item is to a current physical location of the user, and the social graph relevance is based on how close a node representing the information item is to a node representing the user in a graph associated with the social networking system, the graph representing information contained in the social networking system;

send one or more push notifications to one or more client devices associated with the user, the push notifications comprising one or more of the information items sent in an order of their respective relevance values.

18. The system of claim 17, wherein the information items comprise a first information item and a second information item,

the importance level of the first information item decreases as time passes,

the importance level of the second information item increases or remains constant as time passes, and

a first rank boosting factor that corresponds to the first information item is greater than a second rank boosting factor that corresponds to the second information item.